esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Basic Genetic Algorithm  
By using the basic genetic algorithm, this study solves the target scheduling model for battery-swapping trucks in the open-pit mine, and obtains a transportation scheduling plan for 30 trucks within a single work shift. The operational routes for these trucks under this scheme are illustrated in the figure below. In the scheduling process, 'loading' refers to the time required for the truck to load materials at the loading point, 'unloading' refers to the time the battery-swapping truck spends unloading materials at the unloading point, 'battery-exchange' denotes the time the truck spends at the battery-swapping station to replace the battery, 'loaded driving' represents the driving time of the truck while transporting a full load of materials, and 'unloaded driving' refers to the time the truck spends driving while empty, without carrying any materials.  
In Figure 4, the multi-objective weighted evaluation score stands at 127.04. The total transportation costs for a single work shift amounts to CNV 13,713.89, coupled with a total waiting time of 22.96 h, a transportation distance of 2496.3 km, and 73 battery swaps. On average, each battery-swapping truck incurs a transportation cost of CNY 457.13, waits 0.76 h, travels 83.21 km, and undergoes 2.43 battery swaps. Shovel points are as follows: point A conducted 124 loading operations with a total of 7440 tons, point B conducted 121 operations for 7260 tons, point C conducted 155 operations for 9300 tons, point D conducted 124 operations for 7440 tons, and point E conducted 121 operations for 7260 tons.  
Figure 4: Truck operation Gantt chart using the basic genetic algorithm.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm
### Model Overview  
The model architecture is showed in Figure 1. It first uses the LSTM-CRF model to obtain the tag sequence (LS\({}_{1}\), LS\({}_{2}\)\(\dots\) LS\({}_{n}\)) under the influence of a data drive. The input is a word vector corresponding to a word, and the word vector can be pre-trained or trained together in the model. The output is the label for each word. This process can only get a rough range of each element. The most important idea of the LSTM-CRF model is to add the CRF model as the decoding layer of the model based on Bi-LSTM and consider the rationality between the prediction results. To further obtainaccurate boundaries, the integrity algorithm formed by the POS syntactic rules is used to continuously correct the range of element recognition to obtain the final tag sequence (L1, L2... L_n_).
## 3 Results
### Numerical Simulations and Data Fitting  
We consider experimental data published in [18] and perform numerical simulations in MATLAB(r) and Wolfram Mathematica(r). The reduced model (8) is obtained assuming the cell population to be in equilibrium, that is, for times \(t>t_{cc}\), where \(t_{cc}\) is the time at which bacteria have reached the saturation level. In Figure 2, we read from experimental data that the cell population reaches the equilibrium after _ca._ 20 h from the beginning of the experiment. Hence, we take \(t_{cc}=20\) as the starting time point for numerical simulations of the reduced system (8), and define initial data on the time interval \([t_{cc}-\tau,t_{cc}]\). For simplicity, we assume that initial data are constant functions on the definition interval, see also [17; 18]. We fix the value of the delay, \(\tau=2\) h, as in [18]. Then we take \(x(t)=\hat{x}_{19}\), for \(t\in[18,20]\), \(\hat{x}_{19}\) being the mean value of ELISA and UHPLC measurements at 19 h from the beginning of the experiment. Initial data for the Lactonase are estimated from simulations of the full model (4) in [18]. To date, there is no experimental data available for Lactonase concentration, thus parameters associated with Lactonase production (\(\rho\)), decay (\(\omega\)), and activity (\(\delta\)) can be only estimated from AHL experimental data. This means in turn that there are several plausible solutions for the estimation of \(\rho\), \(\omega\), and \(\delta\). We choose to maintain parameter values as estimated in [18]. It can be seen from the model reduction assumptions, as well as from the simplified parameters (7) that we lack information on the receptor production (\(\alpha_{R}\)) and decay (\(\gamma_{R}\)); indeed, there is no equation for PpuR in (4). These parameters play a role for the critical threshold value, \(x_{th}\), in the complex-regulated processes. We fit \(x_{th}\) and \(y(t)=y_{0}\), \(t\in[18,20]\), fixing all other parameter values as in [18]. The results are summed up in Table 1, with parameter values as provided by the fitting procedure, without rounding. In Figure 3 we show a comparison between the numerical solution of model (4), that of the simplified model (8) and experimental data for AHL time series. With the estimated parameter values in Table 1, we consider the analytical results in Section 3.2 and Section 3.4. The system has three equilibrium points \(\tilde{x}_{1},\tilde{x}_{2},\tilde{x}_{3}\), but we only consider the stability properties of the largest one \((x_{3},y_{3})=(1.593\times 10^{-7},4.809\times 10^{4})\), which corresponds to high AHL level and to an activated state of the bacteria population. Parameter values satisfy \(a^{2}d^{2}>b^{2}c^{2}\), thus there is no stability switch with respect to \(\tau\), and the system behaves as in the case \(\tau=0\). We go back to Section 3.3 and consider the Jacobian matrix (13), obtaining \(tr(J)=-7.4243\) and \(\det(J)=0.7685\). Hence, with the estimated parameter values, the system (8) has a locally asymptotically stable equilibrium \((x_{3},y_{3})\) in which bacteria are activated.
## 3 Preliminaries
### Bfv  
Fully homomorphic encryption(FHE) [19; 34; 35] supports the calculation of ciphertext without decrypting it. The calculation result is also saved and transmitted in ciphertext, and the result of ciphertext decryption is the same as that of plaintext computation, i.e., \(f(Enc(m))=Enc(f(m))\). In the BFV scheme [34], the ciphertext consists of polynomials in the ring \(R_{q}\). The plaintext is a polynomial in the ring \(R_{t}\), and the BFV encryption scheme relies on several system parameters, including \(d\), \(q\), \(t\), and \(\sigma\), to ensure both correctness and security. These parameters are chosen carefully to balance the level of security with the efficiency of the encryption process.  
The BFV encryption scheme includes the following algorithms.  
(1) \((pk,sk)\leftarrow\)\(BFV.KeyGen(1^{\lambda})\): Randomly sample \(\mathbf{s}\stackrel{{ R}}{{\leftarrow}}R_{2}\) and set the private key \(sk=\mathbf{s}\). Then, randomly sample \(\mathbf{pk_{1}}\stackrel{{ R}}{{\leftarrow}}R_{q}\) and a noise vector \(\mathbf{e}\stackrel{{ R}}{{\leftarrow}}\chi\). Compute and obtain the public key \(pk=(\mathbf{pk_{0}},\mathbf{pk_{1}})=\left([-(\mathbf{pk_{1}}\cdot\mathbf{s}+ \mathbf{e})]_{q},\mathbf{pk_{1}}\right)\).  
(2) \(ct\gets\)\(BFV.Enc(pk,\mathbf{m})\): To encrypt a message \(\mathbf{m}\) that belongs to the polynomial ring \(R_{t}\), random vectors \(\mathbf{u}\), \(\mathbf{e}_{1}\), and \(\mathbf{e}_{2}\) are sampled uniformly at random from the sets \(R_{2}\) and \(\chi\), respectively. Let \(\triangle=\lfloor q/t\rfloor\), and compute  
\[CT=(CT[0],CT[1])=([\mathbf{pk_{0}}\cdot\mathbf{u}+\mathbf{e}_{1}+\triangle \cdot\mathbf{m}]_{q},[\mathbf{pk_{1}}\cdot\mathbf{u}+\mathbf{e}_{2}]_{q})\]  
(3) \(BFV.Dec(CT)\): Compute  
\[\left[\left\lfloor\frac{t\cdot[CT[0]+CT[1]\cdot sk]_{q}}{q}\right\rceil\right]_ {t}\]  
(4) \(BFV.Add(CT_{1},CT_{2})\): The \(Add\) algorithm adds up the ciphertexts \(CT_{1}\) and \(CT_{2}\), returning  
\[\left([CT_{1}[0]+CT_{2}[0]]_{q^{\prime}}[CT_{1}[1]+CT_{2}[1]]_{q}\right)\]  
(5) \(BFV.Mul(CT_{1},CT_{2},rlk)\): The \(Mul\) algorithm multiplies the corresponding terms of the two ciphertexts \(CT_{1}\) and \(CT_{2}\) and then reduces the ciphertext using the relinearized key \(rlk\).
## 6 Conclusions  
The analysis confirms the growing interest in the application of artificial intelligence for the assessment of students' attentional state and its potential to improve teaching and pedagogical decision-making. Most of the studies reviewed employed experimental methodologies based on automated observation and data analysis using advanced algorithms, allowing for more accurate identification of attention patterns. However, there remain challenges related to privacy, implementation in real educational settings, and the validation of these technologies in different contexts. Scientific production is concentrated in certain regions, which highlights the need to extend studies to other educational systems. Despite the progress made, the diversity of approaches and tools used hinders comparison of the results, highlighting the need for standardised models to enhance their applicability.  
In addition to the findings obtained, this review identifies relevant gaps in the literature and formulates useful recommendations for advancing the use of AI in education, such as the following. Firstly, there is a need to design more accessible and ethically sustainable artificial intelligence systems. These systems must be able to be effectively integrated into real classrooms without compromising student privacy or autonomy. Secondly, it is recommended that teacher training in the critical and pedagogical use of these technologies be strengthened. This preparation is key to avoiding excessive dependence on automated systems and to maintaining the role of teachers as mediators of learning. Thirdly, it is suggested that future research adopt a comparative and interdisciplinary approach in order to analyse the effectiveness of these systems at different educational levels and in different sociocultural contexts.  
In summary, the application of AI technologies to measure student attention represents a promising avenue for personalising teaching and improving educational processes. However, their effective integration requires ensuring ethics, equity, and the active participation of teachers. In the short term, pilot projects should be launched in real environments, and collaboration between disciplines should be promoted to ensure responsible development. In addition, future research should consider the technical effectiveness as well as the emotional, social, and educational impact of these tools in diverse contexts.  
Similarly, recommendations can be made for different stakeholders. It is a priority for education professionals to receive training in the critical and pedagogical use of AI technologies, integrating them as a support for teaching judgement and not as a substitute for it. Policy makers should promote clear regulatory frameworks that ensure privacy protection and fairness in the implementation of these tools, especially in contexts involving minors. Finally, the research community is encouraged to expand studies in diverse contexts, explore the longitudinal impact of these technologies on learning, and move towards more transparent, ethical, and culturally sensitive models.
# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 2 Materials and Methods
### EMCF-RTDETR Model  
To achieve simultaneous improvement in accuracy and real-time performance for soybean weed detection, this paper chooses ResNet-18 as the backbone network of RT-DETR [34]. ResNet-18 has the advantages of faster computation speed and smaller model size compared to networks such as ResNet-L, ResNet-101, and ResNet-50 [38, 39]. The EMCF model structure is illustrated in Figure 2. The CGF-Block significantly improves the backbone network by combining the hybrid channel mechanism with FasterNet Block [40]. This innovation not only reduces redundancy and computational complexity but also enhances feature extraction through multi-channel information processing. Furthermore, the AIFI module is refined by introducing the Efficient Additive Attention mechanism [41], which makes AIFI more flexible and fast in feature interaction. Finally, the innovative EMFF network employs differentiated processing strategies, effectively preserving the
## Materials and Methods
### Membrane Characterization
#### 2.5.3 Determination of Antioxidant Activity  
The antioxidant activity of fabricated membranes was evaluated by using DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS free radical scavenging assay described by [PERSON] et al. (2008), with some modification. A portion of 30 mg membrane samples were mixed with 5 mL of DMSO in glass tubes and sonicated in a bath type sonicator for 3 h, and subsequently centrifuged (SkySpin CM-6 MT centrifuge, ELMI Ltd., Riga, Latvia) at 3500 rpm for 10 min. Afterwards, 7.4 mg of DPPH was dissolved in 100 mL of methanol for an absorbance of 1.8 at 520 nm. After that, the absorption intensity of 4 mL of DPPH and an aliquot of 800 \(\mu\)L of supernatant was measured at 520 nm. Equation (1) was used to determine the percentage of DPPH free radical quenching activity (\(S(\%)\)):  
\[S(\%)=\frac{A_{control}-A_{sample}}{A_{control}}\times 100 \tag{1}\]  
where \(A_{control}\) and \(A_{sample}\) are the absorbance of methanoic solution of DPPH and the mixure of sample extract and DPPH solution at 520 nm, respectively.  
For analyzing the ABTS free radical scavenging activity, a mixture of 10 mL of 7 mM ABTS aqueous solution in 10 mL of 2.54 mM potassium persulfate aqueous solution was first prepared and kept in the dark for 16 h (\(ABTS^{*}\)). After that, the absorbance of \(ABTS^{*}\) was adjusted to 0.7 \(\pm\) 0.02 by diluting with DMSO. Finally, the absorbance of an aliquot of 40 \(\mu\)L supernatant and 4 mL diluted \(ABTS^{*}\) after 5 min vortex was measured at 770 nm with a UV-vis spectrometer (Jasco V-670 Spectrophotometer, Tokyo, Japan). DMSO was used as the blank and the ABTS\({}^{*}\) antioxidant capacity was evaluated based on Equation (2):  
\[ABTS^{*}antioxidantcapacity(\%)=\frac{A_{control}-A_{sample}}{A_{control}}\times 100 \tag{2}\]  
where \(A_{control}\) and \(A_{sample}\) are the absorbance of the mixture of \(ABTS^{*}\) with DMSO and the mixture of sample extract and \(ABTS^{*}\) solution at 770 nm, respectively.
## 2 Design of the System  
### Optical Design  
The optical system was based on a combination of two optical elements that concentrated the incident solar irradiance onto a linear-focus receiver. Figure 1 illustrates the cross-section of the two designed optical systems. The first element, where the rays initially arrive in Figure 1, had two different surfaces, one on the exterior and the other in the interior. The external one was a half cylinder, and the internal one was a free-from non-transverse profile surface that was optimised as a function of the inner liquid. The second element was a dielectric liquid (coloured in blue in Figure 1) that filled the cavity formed between the solid internal surface of the first optical element and a cylindrical transparent casing. In other words, from the outside, the optical system looked like a cylindrical rod. The cylindrical casing was made of polymethyl methacrylate (PMMA), and there were two configurations: one had the cylinders filled with deionised water (DIW) and the other with isopropyl alcohol (IPA). In the next subsection, the motivation to choose these two liquids is explained.  
The design requirements of the optical system are as follows (further details about the whole design process can be found in reference [36]):  
* To focus the incident solar irradiance onto the PV receiver, the optical efficiency and geometric concentration ratio must be maximised while minimising chromatic aberration and maintaining focal length inside the cavity.
* The acceptance angle has to be high enough so that the system is able to use low-accuracy (cheap) solar trackers. In this way, the PV cells are cooled down by direct immersion in the optical liquid medium. The free-form profile of the solid optical element was optimised by means of a genetic algorithm. The goal was to meet the requirements previously mentioned, considering PMMA casing and the two selected dielectric liquids.  
In Table 1, the main optical parameters obtained in the optical study are shown. The geometric concentration ratio (\(C_{\mathrm{g}}\), the quotient between aperture area and PV-cell area) of the design was 12, and the optical efficiency (\(\eta_{\mathrm{o}}\), relation between the power reaching the PVs and the incident power) was 76% for DIW and a bit higher in the case of IPA (80%). The non-uniformity parameter (\(\alpha\)), calculated as the quotient between the maximum difference of local irradiances at the receiver plane and the sum of the maximum and minimum local irradiances, indicates a higher degree of non-uniformity in the case of IPA (\(\alpha=0.22\)), but 0.22 is considered to be an acceptable \(\alpha\) value.  
Figure 1: Schematic of the optical system design for a 12\(\times\) geometric concentration with DIW.
###### Abstract  
The appearance of a large number of mobile intelligent devices boosts the fast rise of mobile health (mHealth) application. However, due to the sensitivity and complexity of medical data, an efficient and secure mobile communication mode is a very difficult and challenging task in mHealth. The Opportunistic Networks (OpNets) is self-organizing and can expand the communication capacity by the movement of nodes, so it has a good prospect in the application of mHealth. Unfortunately, due to the shortage of stable and reliable end-to-end links, the routing protocol in OppNets has usually lower performance and is unsafe. To address these issues, we propose an adaptive routing optimization algorithm in OppNets for mHealth. This routing scheme firstly analyzes the relationship between nodes and defines the average message forwarding delay as a new metric to selectively forward messages, and then designs a local community detection algorithm based on the metric to adapt to the characteristics of OppNets, and finally resorts to some super-nodes to ferry messages between different communication domains. The simulation results demonstrate the efficiency and effectiveness of the proposed scheme. It increases the delivery ratio by about 30%, decreases delay by about 35%, and decreases the number of forwarding by about 5%, by comparing it with several existing routing schemes. We believe that the relationship between nodes, community, and message ferrying will play an important role in routing of OppNets for mHealth.  
mobile health; opportunistic networks; relationship; community; message ferry +
Footnote †: journal:  
12 March 2019; Accepted: 17 April 2019; Published: 19 April 2019
# Reference Data by Player Position for an Ice Hockey-Specific Complex Test
## 3 Results
### Performance Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Defenders** & **Forwards** & \multirow{2}{*}{**\(p\)**} & \multirow{2}{*}{**\(\mathbf{\eta_{p}}^{2}\)**} & \multirow{2}{*}{**d**} \\  & **(n = 40)** & **(n = 64)** & & & \\ \hline  & \multicolumn{4}{c}{**Load parameters**} & & \\
10 m sprint without puck [s] & 1.93 \(\pm\) 0.11 & **1.88**\(\pm\) 0.10 & 0.040 & 0.041 & 0.48 \\
30 m sprint without puck [s] & 4.49 \(\pm\) 0.18 & **4.44**\(\pm\) 0.16 & 0.119 & 0.024 & 0.29 \\
10 m sprint with puck [s] & 1.92 \(\pm\) 0.13 & **1.90**\(\pm\) 0.12 & 0.371 & 0.008 & 0.16 \\
30 m sprint with puck [s] & 4.60 \(\pm\) 0.18 & **4.56**\(\pm\) 0.18 & 0.206 & 0.016 & 0.22 \\
10 m backward sprint without puck [s] & **2.34**\(\pm\) 0.12 & 2.40 \(\pm\) 0.19 & 0.078 & 0.030 & 0.39 \\
30 m backward sprint without puck [s] & **5.43**\(\pm\) 0.23 & 5.57 \(\pm\) 0.30 & 0.012 & 0.060 & 0.53 \\ Transition without puck [s] & 17.3 \(\pm\) 0.72 & **17.2**\(\pm\) 0.92 & 0.505 & 0.004 & 0.12 \\ Transition with puck [s] & 18.8 \(\pm\) 1.03 & **18.7**\(\pm\) 0.97 & 0.438 & 0.006 & 0.01 \\ Weave without puck [s] & 22.9 \(\pm\) 0.83 & **22.4**\(\pm\) 0.86 & 0.008 & 0.067 & 0.59 \\ Weave with puck [s] & 23.7 \(\pm\) 0.69 & **23.1**\(\pm\) 0.88 & **<0.001** & **0.122** & 0.76 \\ slap shot 1 before test [km\(\cdot\)h\({}^{-1}\)] & **127**\(\pm\) 10.14 & 124 \(\pm\) 10.1 & 0.273 & 0.012 & 0.29 \\ slap shot 2 before test [km\(\cdot\)h\({}^{-1}\)] & **127**\(\pm\) 10.1 & 126 \(\pm\) 9.68 & 0.464 & 0.005 & 0.10 \\ slap shot 3 before test [km\(\cdot\)h\({}^{-1}\)] & **129**\(\pm\) 9.33 & 127 \(\pm\) 10.2 & 0.490 & 0.005 & 0.21 \\ wrist shot 1 before test [km\(\cdot\)h\({}^{-1}\)] & **105**\(\pm\) 6.67 & 105 \(\pm\) 7.07 & 0.711 & 0.001 & 0 \\ wrist shot 2 before test [km\(\cdot\)h\({}^{-1}\)] & 105 \(\pm\) 6.92 & **105**\(\pm\) 6.63 & 0.680 & 0.002 & 0 \\ wrist shot 3 before test [km\(\cdot\)h\({}^{-1}\)] & **105**\(\pm\) 6.70 & 105 \(\pm\) 7.01 & 0.650 & 0.002 & 0 \\ goals before test & 4.19 \(\pm\) 1.01 & **4.26**\(\pm\) 1.08 & 0.742 & 0.001 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Differences in load and stress parameters (Mean \(\pm\) standard deviations (SD)) by playing position. Significant differences (\(p\leq 0.002\)) and (performance) maxima marked in bold.
## 2 Materials Studied  
### Caofeidian Dredger Fill  
Caofeidian belongs to Tangshan City, Hebei Province, China. It is located on the southern coast of Tangshan and in the centre of Bohai Bay (Figure 1). The elevation of the reclamation area is approximately 4.5 m, and the area is flat overall. Based on the site survey (20 m depth), it can be stated that it mainly contains silty sand, silt, and silty clay layers formed by quaternary Holocene sedimentary layers (Q4m), except the surface dredge fill (Q4 ml).  
The dredge fill material studied in the present work is extracted from a site that is 50 m north of Caofeidian Station (39\({}^{\circ}\)2\({}^{\prime}\)13\({}^{\prime\prime}\) N, 118\({}^{\circ}\)30\({}^{\prime}\)34\({}^{\prime\prime}\) E). The soil is extracted from a depth of approximately \(-\)2.0\(--\)1.5 m. The material is representative because it only has a period of self-weight consolidation without reinforcement. The geotechnical parameters of the material studied are determined following the Chinese Standard JTG E40-2007, as shown in Table 1. The components of soluble salt are calibrated following the Chinese Standard GB/T 50123, as presented in Table 2.  
Table 1 shows that the natural water content of the Caofeidian dredger fill is 26.1% (which is close to the liquid limit), and its plastic index is 12.69. Therefore, the material can be classified as low-liquid-limit clay based on ASTM 2487 (classification of fine-grained soil) [21]. According to Table 2, it can be stated that Na\({}^{+}\) and Mg\({}^{2+}\) are the main cations and Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\) are the main anions in the soluble salt, and that the main soluble salts are NaCl, MgCl\({}_{2}\), and sulphate. The total salt content of the Caofeidian dredger fill is 1.3%.