esa-sceva/satcom-chunk-collection huggingface dataset:
# A New \"Doctor and Patient\" Optimization Algorithm: An Application to Energy Commitment Problem
## 6 Conclusions  
A new doctor and patient optimization (DPO) Algorithm was introduced based on a simulation of the patient treatment process. This treatment process has three phases including vaccination, drug administration, and surgery. To evaluate the effectiveness and performance of the DPO, two case studies were considered. In case study A, the performance and effectiveness of the proposed DPO algorithm was evaluated on a benchmark standard test function with twenty-three objective functions and compared to eight other algorithms. These results show the exploitation and exploration capacity of the proposed algorithm in solving optimization problems. In case study B, the proposed DPO algorithm was implemented on the energy commitment (EC) problem in a power system with twenty-six power plants and various energy sectors, including residential, commercial, public, industrial, transportation, and agriculture sectors. The purpose of the EC was to determine the appropriate pattern of use of energy carriers to supply energy demand and minimize operation costs considering the technical constraints. The DPO with high exploitation and exploration capacity was well implemented on the EC problem, and its results were determined including the appropriate pattern of use of energy carriers, proper composition, and production of power plants, as well as the amount of import and export of energy carriers.  
In future works, the authors propose several study ideas, such as solving the EC problem using other optimization algorithms and techniques, creating a binary variant of the DPO which has an important potential contribution, and applying DPO to overcome many-objective real-life optimization problems, as well as multi-objective problems.  
Conceptualization, M.D., M.M., J.C.V. and J.M.G.; methodology, M.D.; software, M.D.; validation, R.A.R.-M., J.M.G., L.P.-A., and O.P.M.; formal analysis, J.M., R.A.R.-M., L.P.A., and O.P.M.; investigation, M.D., J.C.V. and O.P.M.; resources, R.A.R.-M.; data curation, R.A.R.-M.; writing--original draft preparation, M.D. and M.M.; writing--review and editing, J.M., J.C.V., O.P.M., L.P.-A., and R.A.R.-M.; visualization, M.D.; supervision, M.M.; project administration, M.D. and M.M.; funding acquisition, R.A.R.-M. All authors have read and agreed to the published version of the manuscript.  
The current project was funded by Tecnologico de Monterrey and FEMSA Foundation (grant CAMPUSCITY project).  
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 6 Conclusions  
To study the cold-bending stress and its reverse-coupling effect with the uniform load on SGP laminated glass panels, single-corner cold-bending tests, uniform load tests, and ultimate capacity tests were performed on eight SGP laminated glass panels with different cold-bending curvatures and interlayer thicknesses. The conclusions of this study are as follows:  
1. The cold-bending stress of SGP laminated glass panels under single-corner cold-bending exhibits a saddle-shaped distribution, in which the maximum and second- greatest cold-bending stresses appear near the corner of the short side and in the long side direction adjacent to the cold-bending corner, respectively.
2. Since the bearing capacity of the glass panel is controlled by its maximum principal tensile stress and the bearing capacity is smaller on the edges compared to the panel center, the two adjacent corner points of the cold-bending corner should be monitored and restricted with utmost care and priority during cold bending for the cold-bending stress.
3. Due to the geometrical nonlinearity effect caused by cold bending, the cold-bending stress and coupling stress increase nonlinearly with the rise in cold-bending curvature. Meanwhile, both the cold-bending stress and coupling stress are positively correlated to the interlayer thickness. Besides, cold-bending curvature has a more significant impact on the cold-bending stress and coupling stress than the interlayer thickness.  
Figure 11: The effect of cold-bending curvature. (**a**) Ultimate capacity curves; (**b**) Ultimate deflection curves.  
* As the uniform load increases gradually, the coupling stress distribution changes from a profile similar to the cold-bending stress distribution to another of four edges, which is simply supported under a uniform load.
* After the reverse coupling of cold bending and uniform load, the bearing capacity of the SGP laminated glass panels clearly dropped under the influence of the geometrical nonlinearity effect caused by the cold bending. When the coupling stress at the corner of the short-side direction adjacent to the cold-bending corner is greater than (or less than) that of the panel center, the bearing capacity of the glass panel will be controlled by the cold-bending curvature (or uniform load).
* The ultimate capacity and ultimate deflection of the cold-bent SGP laminated glass panel decrease with greater cold-bending curvature and interlayer thickness. When the cold-bending curvature exceeds 1.0%, the effect of interlayer thickness on the ultimate capacity and ultimate deflection can be ignored.  
Methodology, X.Z.; software, C.Z. and X.Y.; validation, X.Z., X.Y. and C.Z.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, C.Z. and X.Z.; writing--review and editing, X.Z.; project administration, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Guangxi Province (Grant No. 2018 GXNSFAA138204).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interest.
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
## 3 Experiments and Results
### Ex Vivo Porcine Larynx Experiments:  
The ex vivo porcine larynx was imaged before and after laser ablation. In total, over 25 tests on three ex vivo porcine larynx samples were carried out. The imaging area is indicated in Figure 4A; the corresponding white light image of the laryngeal surface is shown in Figure 4B; and the cross-sectional OCT image of the black line area is shown in Figure 4C. In the OCT image, three layers can be distinguished: the epithelial layer (E), the mucosa lamina propria (LP), and the smooth muscle layer (SM). The boundary was obvious. Moreover, the basement membrane (BM) in the OCT image was clear, which was the boundary between the epithelial layer and the intrinsic mucosa layer. Some studies have shown that malignant lesions lead to the disappearance of the BM [17; 31; 32]. Therefore, the coaxial dual-modality images could provide more useful information for diagnosis within one test process.  
An ablation power of 15 W was utilized to test the ablation function. The ablation zone (AZ) in Figure 5A indicates the ablation area. After ablation, a round ablation spot can be seen in the white  
Figure 4: Ex vivo porcine larynx experiment. (**A**) Picture of the ex vivo porcine larynx; (**B**) white light laryngoscope image; (**C**) OCT image of the black line region. E: epithelial layer; LP: mucosa lamina propria; SM: smooth muscle layer; BM: basement membrane.  
Figure 3: (**A**) The test target white light image at 15 mm; (**B**) Optical coherence tomography (OCT) point spread function (PSF); (**C**) laser ablation beam profile at 15 mm (scale bar: 1000 \(\upmu\)m).  
light image. In the OCT image, the ablation effect and boundary can be presented more accurately, as shown in Figure 5B. The preliminary results showed that the multimodal endoscope could be a useful tool for an accurate guiding of laser ablation surgery and could evaluate the ablation effect after surgery.
## 2 Related Work
### ACO-Based Routing Protocol  
In combinatorial optimization, ant colony optimization (ACO) [17] emerges as a prominent swarm intelligence approach inspired by the foraging behavior of ants. As ants explore paths near their nest, they release chemicals known as pheromones. These pheromones serve as cues for other ants to navigate back to the next. Moreover, when an ant discovers a new food source, it leaves a trail of pheromones while transporting the food back to the nest, enabling other foraging ants to follow the scent and locate the food source. However, these pheromones are volatile and evaporate over time. Ants tend to favor paths with stronger pheromone odor among multiple paths, allowing them to identify the shortest route from the nest to the food source. ACO operates as a heuristic algorithm to obtain effective solutions to challenging combinatorial optimization problems within a reasonable computational time.  
In prior research on VANET routing protocols, ACO has been utilized to discover optimal routes. VACO (vehicular routing protocol based on ant colony optimization) [32; 33] was introduced to assess the relay quality of road segments between junctions within an urban environment. This protocol operates under the assumption that RSUs are deployed at each junction to facilitate the identification of the optimal packet path. The source node dispatches multiple forward ants toward the target RSU nearest the destination to establish a route. Subsequently, backward ants gather relay quality information for each road segment and relay it back to the source node through the designated target RSU. The source node can then aggregate data from the RSUs to determine the optimal route regarding latency, bandwidth, and delivery ratio. However, this protocol entails substantial installation costs, necessitating an RSU at every junction. Moreover, the innate characteristics of ants are forfeited in the event of RSU failure.  
MAR-DYMO (mobility-aware ant colony optimization routing DYMO) [34] represents an enhancement over the existing DYMO (dynamic MANET on-demand routing) [35] by introducing an ant-based routing algorithm capable of predicting route lifetime through the utilization of position and speed information of vehicles. Each vehicle determines a phenomenon level that forecasts route lifetime by considering the position, speed information, and the likelihood of message reception. Based on these pheromone levels, The source node determines the optimal route to the destination node. After the path lifetime, an evaporation mechanism is implemented to dissipate the pheromone completely. However, this mechanism suffers from limitations such as high overhead and limited scalability, mainly when the source node must discover a new route following a link failure.
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 2 Milk Macro-Constituents
### Proteins  
Milk contains a variety of proteins that play essential roles in its nutritional and functional properties [33; 34]. Around 200 different proteins have been isolated in milk, with casein being the most abundant [35]. The amino acids that compose them are essentially in the L-\(\alpha\) conformation [36]. All the other proteins different from casein are known as whey proteins and include \(\beta\)-lactoglobulin and \(\alpha\)-lactalbumin, known for their gelation  
\begin{table}
\begin{tabular}{l c} \hline
**BCFA * & **Content (mg/Three Daily Servings)** \\ \hline iso-13:0 & 5–7 \\ \hline _anteiso-13:0_ & 15–19 \\ \hline iso-14:0 & 18–48 \\ \hline iso-15:0 & 29–97 \\ \hline _anteiso-15:0_ & 81–206 \\ \hline iso-16:0 & 38–100 \\ \hline iso-17:0 & 58–123 \\ \hline _anteiso-17:0_ & 24–169 \\ \hline iso-18:0 & 2–20 \\ \hline \end{tabular}  
* iso- refers to structures where the branch point is on the second-to-last carbon atom (i.e., one carbon from the end), while _anteiso-_ refers to structure where the branch point is on the third-to-last carbon atom (i.e., two carbons from the end).  
\end{table}
Table 1: Branched-chain fatty acids (BCFAs) composition and content in cow’s milk. The content of FAs is expressed as mg/three daily servings. Reproduced under the terms of the Creative Commons CC BY license [22].  
Figure 1: Structure of linoleic acid and examples of conjugated linoleic acids, i.e., _cis_-9, _trans_-11-CLA and _trans_-10, _cis_-12-CLA.  
properties. Measuring the individual whey protein fractions in whey streams is helpful for monitoring and enhancing the effectiveness of whey fractionation processes [37].
## 3 Copper
### Other Copper-Catalyzed Reactions  
[PERSON], [PERSON] and co-workers have developed an efficient protocol for the asymmetric alkynylation of imines [19, 20], using a copper-pybox ligand (**5**, Scheme 4), where the ligand was attached to the polystyrene support via a click reaction. Enantioselectivity were somewhat lower than the corresponding reaction in solution, but a range of different imines and alkynes could be coupled with good to excellent conversion.  
**Scheme 4.** Copper-catalyzed enantioselective alkynylation of an imine using a tethered copper-pybox complex.  
An interesting derivatization of propagylic alcohols via treatment with supercritical carbon dioxide has been reported on solid support by [PERSON] _et al._, who applied copper(I) iodide bound to a dimethylamino-polystyrene resin to form \(\alpha\)-alkylidene cyclic carbonates (Scheme 5) [21]. Optimum CO\({}_{2}\)-pressure for the reaction was found to lie in the range of 14-18 MPa, producing a wide selection of cyclized products in good yields. The reaction was limited to terminal secondary propagylic alcohols with aliphatic substitutents; no product was obtained when 2-phenyl-3-butyln-2-ol was used as the substrate, nor when primary or internal alkynes were applied in the reaction. A brief mention shall also be made of a report by [PERSON] and co-workers, showing that copper(II) coordinated to a polymer-bound proline can catalyze the cross-coupling of oximes with arylboronic acids in reasonable yields [22].  
**Scheme 5.** Fixation of carbon dioxide in the form of a cyclic carbonate, catalyzed by a polymer-supported Cu(I)-amine complex.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Software Level  
Energy-saving techniques at the software level include OS-based scheduling, duty cycling, batching, parallel thread processing, and low-power software. Markov Decision Process (MDP) is widely used for resource and power optimization in WSN [23]. It can drains energy with setting a priority scheduling and medium access [24]. The sensing frequency and transmission power are changed with the supply voltage and frequency to recover the node's new operating state as environmental stimuli change. It can also determine the appropriate time to load a sensor node as in [25].
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results
### BSWSS-Net
#### 5.2.2 Complexity Analysis of WSS Algorithms  
To compare the computational complexity of different WSS algorithms, the FLOPs, running time, video memory usage, and the number of learnable parameters are evaluated for DeepSense [21], ParallelCNN [22], CNNWSS-Net [18], and BSWSS-Net. In the TVWS dataset, the size of label \(N_{c}=160\), while in the NR dataset, the size of label \(N_{c}=132\). Thus, we evaluate the computational complexity in these two datasets separately. The input signal length is set to 2048 for both datasets. We implemented these algorithms on a PC equipped with an Intel Core i9-13900 KF 3 GHz CPU and two Nvidia GeForce RTX 4080 Super GPUs to evaluate their runtime. As shown in Table 3, compared to the NR dataset, the number of FLOPs, the number of learnable parameters, video memory usage, and running time required by several algorithms are slightly higher when evaluated in the TVWS dataset. This is because the size of the label is slightly larger in the TVWS dataset. The FLOPs required by BSWSS-Net are approximately half of those required by the other three algorithms, with the fewest learnable parameters and video memory usage. Due to the low video memory usage of BSWSS-Net, it can be efficiently deployed on embedded devices.
## 5 Experiment and Analyses  
### Training Details  
The next-generation simulation (NGSIM) dataset, comprising vehicle trajectory data from the US101 and I-180 highways collected through a network of synchronized digital cameras [10], was adopted for model training in this study. The vehicle trajectory data records are provided for every one-tenth of a second. This dataset contains a large number of vehicle-following and lane-changing scenarios in dense traffic, which effectively reflects interaction between vehicles and other traffic participants. For the prediction model proposed in this paper, the vehicle historical data of the past 3 s is applied to predict the vehicle trajectory for the next 5 s.  
To quantitatively illustrate the accuracy and reliability of the prediction model proposed in this paper, the root mean squared error (RMSE) metric between the predicted position and ground truth trajectory position within the prediction range for the prediction horizon is chosen as the evaluation index used for evaluation  
\[L_{\text{RMSE}}=\sqrt{\frac{1}{t_{pre}}{\sum_{1}^{t_{pre}}{\left\|\tau_{g}^{t} -\tau_{o}^{t}\right\|}^{2}}} \tag{27}\]  
where \(\tau_{g}^{t}\) means the position of the ground truth trajectory at time \(t\), while \(\tilde{\tau}_{o}^{t}\) denotes the position generated by the prediction model at time \(t\).
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 4 Towards Algorithm Development: Context  
Let us assume that we have \(N_{m}=n_{UAV}+n_{USV}\) mobile unmanned vehicles (drones, boats and other surface vehicles) \(UV_{1},UV_{2},UV_{3},\dots,UV_{i},\dots,UV_{N}\) that are able to monitor  
Figure 1: FSANET-WSN (Flying and Surface Ad Hoc NETwork serving Wireless Sensors Networks) architecture.  
an area \(A=A_{1}\cup A_{2}\cup\ldots\cup A_{N}\), where \(A_{i}\cap A_{j}\
eq\emptyset\). Moreover, we have \(M\) WSNs that communicate with the Cloud Center through a sink node. Some of the sink nodes can be connected directly to the same gateway (noted further as GW) as the Cloud Center, whereas others can be connected at a certain time to the FSANET network, whose nodes become access points for WSNs. The GPS coordinates (latitude, longitude) and the height of the GW and WSNs antennae to the ground are known and stored in the CC database.  
The location of each unmanned vehicle is queried on demand by the CC. Thus, the CC triggers the UVs based on a particular message through LoRa communication. In exchange, UVs send their current locations. The CC executes the position optimisation algorithm and sends a set of relative coordinates (offsets) to each UV in range. Specifically, in the case of UVs, the latitude offset (\(\Delta\phi\)) and longitude offset (\(\Delta\lambda\)) are sent, whereas for aerial vehicles, an extra parameter, altitude offset, is provided (\(\Delta a\)).  
The main concern is to connect a certain WSN to the gateway with the help of UVs acting as access points.  
UVs are autonomous in the limits of LoRa coverage. When the LoRa communication link is not available, the UVs are programmed to return to a predefined location in the range of the gateway.  
The unmanned vehicles can act as relays when the communication between a specific unmanned vehicle or WSN and the gateway is not possible or when the communication between unmanned vehicles themselves cannot be assured through a direct link. In Figure 2, an example of the positioning of five UAVs, two USVs, one GW and five WSNs is emphasised. On the x-axis, the longitude was represented, whereas on the y-axis, the latitude was represented. The z-axis was reserved for antenna height to the ground. For reproducibility, throughout the paper, all the positions of the UVs and of the fixed nodes (GW and WSN) are generated based on pseudorandom number generators with known states.
# Facility Layout Problem with Alternative Facility Variants
## 4 Proposed Method
### Mutation  
In order to reflect the extended representation and the fact that the search space significantly increases with the introduced workstation alternatives, we have added a new mutation operator to the evolutionary algorithms. This operator swaps two elements of the priority list associated with the same workstation. Given the parental priority list \(P\) as a permutation of set \(T\), see (1), the mutation works in the following steps:  
1. Randomly choose a position \(k\) within the parental priority list \(P\);
2. Find a set of positions \(S_{k}\) in \(P\), excluding the position \(k\), that are associated with the same workstation as the element \(P[k]\), \(S_{k}=\{l:(P[l].\texttt{id}=P[k].\texttt{id})\wedge(l\
eq k)\}\);
3. Randomly choose one position \(j\) from \(S_{k}\);
4. Swap elements at positions \(j\) and \(k\).  
If one of those two swapped elements is the left-most occurrence of the workstation in the priority list, then this action most likely leads to the effective modification of the corresponding floor plan. Otherwise, the mutation operation does not affect the floor plan represented by the priority list. This is a so-called silent mutation, and it has been shown to be beneficial for preserving diversity within the population in evolutionary algorithms [37; 38].  
Note that each workstation variant can be present in the priority list with only 1 out of 12 configurations, given there are 6 possible orientations and 2 placement heuristics. Variants present in the priority list can change their configuration via standard mutations that change the orientation and heuristic. Importantly, this new mutation operator works with particular workstation variant configurations, as they are stored in the priority list. By executing the effective mutation, a new variant of a certain workstation with its particular configuration becomes active (i.e., will be used in the floor plan). However, the other one, which becomes inactive, stays intact with its configuration in the priority list, thus available for its later re-use.
## 3 Modeling Approaches  
### Bagging  
Bagging is based on the concepts of bootstrapping and aggregating, and is one of the most popular ensemble algorithms [53]. In bagging, the training set is randomly sampled \(n\) times with replacement, producing \(n\) training sets with sizes equal to the original training set [53].  
In this study, we considered a learning set \(C\) consisting of \(n\) independent observations [54; 55]. Here, they are landslide conditioning factors, so we have \(C=\{(X_{i},Y_{i}),i=1,2,\ldots n\}\). Firstly, set \(C_{b}\) (\(b\) = 1,2, \(\ldots\), \(n\)) denotes the \(b\)-th bootstrap sample of the training set \(C\) obtained by drawing with replacement \(n\) elements of \(C\). Secondly, compute the bootstrapped estimator \(g*(\cdot)\) by the plug-in principle: \(g*(\cdot)=h_{n}((X_{1}^{*},Y_{1}^{*}),\ldots(X_{n}^{*},Y_{n}^{*}))(\cdot)\). Finally, repeat the above step \(m\) times, where the \(m\) is often defined as 50 or 100, yielding \(g^{*k}(\cdot)(k=1,2,\ldots,m)\). So, the bagged estimator is \(g_{Bag}(\cdot)=\frac{\sum\limits_{i=1}^{n}g^{*k}()}{m}\).  
In theory, the bagged estimator is described as follows:  
\[g_{Bag}(\cdot)=^{*}[g*(\cdot)], \tag{1}\]  
where the theoretical quantity corresponds to \(\text{m}\rightarrow\infty\) and the finite number \(m\) in practice governs the accuracy of the Monte Carlo approximation.