esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Growth  
The capabilities of the technique were explored by simultaneously investigating the effect that the ion dose and GIS-sample distance have on the nature of the nanostructures. The ion dose \(D\) was calculated as the charge received per area, \(D=I\cdot t/A\), where \(I\) is the ion beam current, \(t\) is the deposition time, and \(A\) is the area of the feature that is to be patterned. The parameter was therefore easy to tune experimentally by modifying the deposition time according to the dimensions of the pattern and the used ion beam current. The standard value of \(D\) in previously reported studies for Cryo-FIBID of W(CO)\({}_{6}\) is 55 \(\upmu\)C/cm\({}^{2}\).  
The effect of the dose was tested by growing four \(4\times 4\)\(\upmu\)m\({}^{2}\) W-C squares with a fixed ion beam current of 7.7 pA and varying the ion dose between 10 and 500 \(\upmu\)C/cm\({}^{2}\) (Figure 2). All irradiation times are in the order of seconds, whereas growing one of these squares at room temperature (with an optimized volume per dose of 0.082 \(\upmu\)m\({}^{3}\)/nC) would require an estimated dose of 61,000 \(\upmu\)m\({}^{3}\)/nC, taking roughly 20 min.  
The 2.5 mm difference in the GIS-sample distance has a profound and evident effect on the integrity of the samples--those grown further apart from the GIS appear to have more holes, with areas where the lack of precursor results in the appearance of cavities. This issue is overcome if the working distance is decreased to 7.5 mm, where the presumably enhanced availability of precursor condensate allows for the deposit to be fully grown. While 10 mm has been shown to be sufficient for Cryo-FIBID of W(CO)\({}_{6}\) at \(-\)100 \({}^{\circ}\)C, the 40 \({}^{\circ}\)C difference appears to require a greater amount of precursor material.  
For both GIS-sample distances, the lowest assessed value of ion dose yields a thin, non-homogeneous layer of material, while the upper end of the studied range results in the appearance of holes. Both effects can be attributed to the condensate layer's under and over-exposure, thus indicating an optimal value for this parameter in the intermediate  
Figure 2: W–C squares deposited with variable ion doses at a distance from the GIS of (**a**) 10 mm, and (**b**) 7.5 mm. The deposition times are, for each square, and in order of increasing dose, 0.2, 1.0, 2.1, 4.2, and 10.3 s.  
region in the 50-200 \(\upmu\)C/cm\({}^{2}\) range. Again, these values are comparable, yet slightly above those retrieved in \(-100\)\({}^{\circ}\)C Cryo-FIBID of W(CO)\({}_{6}\).  
The metallic content of the deposits increases with the ion dose used to grow them, as revealed by EDX measurements performed in independent arrays of W-C squares nominally identical to those shown in Figure 2b (see the Supplementary File). Even though it is not possible to retrieve a precise figure of the amount of W using SEM-EDX only due to the reduced thickness of the deposits, the results are compatible with an atomic W content in the 20-30% range in deposits grown with ion doses of 50 \(\upmu\)C/cm\({}^{2}\) and above.
## 5 Result and Discussion
PCA is a popular and powerful blind source separation algorithm. It constructs a low-dimensional representation of the data that describes the variance in the data as much as possible. It is achieved by finding a linear basis of reduced dimensionality for the data, in which the amount of variance in the data is maximal [37]. To implement the PCA + LS-SVM method, 10 dimensionless time-frequency indexes (described in Table 1) of each signal are calculated as the input of PCA, and 10 principal components transformed by PCA are selected as the input of LS-SVM. The SSA + LDA method substitute LS-SVM for LDA in Algorithm 3, that is to say, LDA is used as the classifier model. LDA is a linear classifier that uses a generalization of [PERSON]'s linear discriminant; its aim is to find a linear combination of features that characterizes classes of objects or events [38]. As shown in Tables 2 and 3, the original LS-SVM, PCA + LS-SVM and SSA + LDA methods are less accurate than the proposed SSA + LS-SVM method. In the original LS-SVM method, the accuracy of the normal, mild wear and severe wear groups with training sets was about 80%, and even low to 65% with testing sets, 22 tools in training sets and 14 tools in testing sets were erroneously categorized. In the PCA + LS-SVM method, the accuracy of the normal, mild wear and severe wear groups with training sets were higher than original LS-SVM method, 20 tools in training sets and twelve tools in testing sets were erroneously categorized. However, with the tool failure gradually becoming serious, its advantage is reduced over original LS-SVM method. In the SSA + LDA method, the accuracy of mild wear group with training sets and testing sets was 65% and 50%, 34 tools in training sets and 23 tools in testing sets were erroneously categorized. The three methods could not discriminate between normal tools and wear tools effectively. The proposed SSA + LS-SVM method in this paper outperformed the original LS-SVM, PCA + LS-SVM and SSA + LDA methods.
## 8 Developed Tool
### Profile Simulation  
In view of the content of the tool, in order to understand the designation of each profile, which will be named according to the P ABCD model, the meaning of each letter is explained below:  
P: profile.  
A: takes values depending on which consumption profile is chosen.  
* Value 0: actual consumption, obtained from the distribution company.
* Value 1: average consumption.
* Value 2: percentage consumption. The term refers to the distribution of consumption in different percentages during the day and night. For example, in the case, 80-20 consumption, what is indicated is that 80% of the consumption takes place during daytime hours, while the remaining 20% occurs during nighttime hours.
* Value 3: BOE Profile. The BOE electricity consumption profile estimates consumers' hourly electricity uses without requiring precise hourly metering, resulting in a fair allocation of costs and billing. The CNMC determines the profile by considering factors such as type of consumer, season, day of the week and daytime hours. It uses hourly weighting coefficients for different periods and types of days, simplifying the billing and electricity consumption management process. The BOE, which is published annually, contains the up-to-date and detailed information necessary for accurate billing and energy allocation.  
B: will take the value 0, if the tariff is the customer's initial tariff without any modification, or 1, if a tariff change is to be made in order to obtain an optimization of the bill price.  
C: will take the value 0 if no photovoltaic installation is envisaged and 1 otherwise.  
D takes values (0, 1, 2, 3) depending on which generation profile is chosen.  
* Value 0: actual generation, based on irradiance data obtained from PVGIS.
* Value 1: average annual generation in sunshine hours.
* Value 2: average monthly generation in hours of sunshine.
* Value 3: generation of a typical year.
# A Survey on Machine Reading Comprehension--Tasks, Evaluation Metrics and Benchmark Datasets
### History  
Machine reading comprehension is not newly proposed. As early as 1977, [PERSON] et al. [10] had already built a question answering program called the QUALM which was used by two story understanding systems. In 1999, [PERSON] et al. [11] constructed a reading comprehension system with a corpus of 60 development and 60 test stories of 3 rd to 6 th-grade material. The accuracy of the baseline system is between 30% and 40% on 11 sub-tasks. Most of MRC systems in the same period were rule-based or statistical models [12; 13]. However, due to the lack of high quality MRC datasets, this research field has been neglected for a long time [14]. In 2013, [PERSON] et al. [15] created the MCTest [15] dataset which contained 500 stories and 2000 questions. Later, many researchers began to apply machine learning models on MCTest [15; 16; 17; 18] despite that the original baseline of MCTest [15] is a rule-based model and the number of training samples in the MCTest [15] dataset is not large. A turning point for this field came in 2015 [14]. In order to resolve these bottlenecks, [PERSON] et al. [19] defined a new dataset generation method that provides large-scale supervised reading comprehension datasets in 2015. They also developed a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure. Since 2015, with the emergence of various large-scale supervised datasets and neural network models, the field of machine reading comprehension has entered a period of rapid development. Figure 2 shows the numbers of research papers on MRC since 2013. As is seen, the number of papers on MRC has been growing at an impressive rate.  
Figure 1: Examples of machine reading comprehension applied to search engine and dialogue system.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation
### Computational Load Analysis  
Finally, we also evaluated the computation efficiency of each scheme. We recorded the total time and useful time of sending all data, then calculated the SDR after transmission. The useful time is divided by the total time and then multiplied by SDR. Further, we could obtain the efficiency ratios. We took the average after several measurements, and finally obtained the results, which are shown in Figure 10.  
In accordance with Figure 10, no matter in the simulation experiment or real experiment, the replication-based scheme has the highest utilization. This is because the scheme does not perform redundant operations, but the video transmission quality is deficient in this case. The proposed scheme in the initial phase is slightly lower than the traditional scheme based on RLNC. With the increase in the number of mobile phones, the utilization rate of our scheme slowly improves and soon outperforms the traditional RLNC scheme.  
To evaluate whether the schemes in this paper can achieve the purpose of load balancing, we calculated the average load value of each scheme. In the experiment, the redundancy at the source nodes in other schemes is set at 3. In our scheme, the value of redundancy is set at 0. The experimental results are shown in Figure 11.  
In the experiment, we calculate the increased load of encoding and decoding. Because the replication-based scheme has no codec operation, the load value of this scheme is always zero. From Figure 11, the average load value of the nodes in our scheme is only half  
Figure 11: Analysis of load balance.  
Figure 10: Analysis of utilization.  
scheme, and this is because the redundant operations of the source nodes are reduced. Besides, as the number of nodes increases, the standard deviation of our scheme declines.
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 4 Results and Discussion
### Critical Stress Intensity Factor  
The critical stress intensity factor (K\({}^{\text{S}}_{\text{IC}}\)) was experimentally determined with a notch beam in a three-point bend test using a closed-loop testing machine under the crack mouth opening displacement control. Load-CMOD curves were plotted from the tests. The K\({}^{\text{S}}_{\text{IC}}\) was calculated using Equation (2). Concrete specimens were tested at 14 and 56 days. Table 5 summarizes the results. It shows that the related tendency of the K\({}^{\text{S}}_{\text{IC}}\) of concrete is similar to that of the G\({}_{\text{F}}\). At an early age (14 days), the K\({}^{\text{S}}_{\text{IC}}\) values of slag concrete are less that of the reference concrete (R0), but exceed that of R0 after 56 days. This implies that the additional pozzolanic reaction of slag with Ca(OH)\({}_{2}\) in concrete becomes active during the period between 14 days and 56 days, which leads to the enhancement of K\({}^{\text{S}}_{\text{IC}}\) of the slag concrete. Moreover, it is seen that the concrete incorporating more slag (40%) presented a lower KSIC value than that with less slag content (20%) for various ages. On the other hand, the concrete containing finer slag can exhibit larger K\({}^{\text{S}}_{\text{IC}}\) values. It can be found from Figure 7 that the increase of the fineness level of the slag leads to an increase in the K\({}^{\text{S}}_{\text{IC}}\) value of concrete for various ages. In other words, the K\({}^{\text{S}}_{\text{IC}}\) value of the mixture with finer slag (S6R2) is in turn higher than those of S5R2 and S4R2 for the similar slag replacement ratio of 20%. Also, the K\({}^{\text{S}}_{\text{IC}}\) of S6R4 > S5R4 > S4R4 for the similar slag replacement ratio of 40%. The reason for this result is that the finer slag particle has a larger surface area, presenting a more active pozzolanic reaction, thus resulting in an increase of strength and fracture toughness.  
Figure 8 compares the critical stress intensity factor with compressive strength for all slag-concrete specimens in the study. As shown in the figure, the two values of stress are nearly linearly related. The relationships shown in Figure 8 are significant because, based on the close relationship between the particle size of slag and compressive strength (Figure 4), the critical stress intensity factor will increase with the increase of the fineness level of the slag.
## 3 Methodology  
### Overview of LoRaWAN and LoRa Technology  
The LoRaWAN Media Access Control (MAC) protocol is an open standard established by the LoRa Alliance, operating above the LoRa physical layer [43]. It outlines the network structure and interactions among three key components of the network:  
* end devices or nodes, which operate in a star topology and gather data that are subsequently transmitted to the network;
* gateways, which serve as coordinators among the nodes, receiving data from end devices and forwarding to the network server;
* LoRaWAN network server, responsible for managing and processing the data.  
The LoRaWAN network architecture is characterized by a star topology, in which end devices communicate solely with LoRaWAN gateways and do not directly interact with each another. These gateways are connected to a central network server and are in charge for transmitting raw data from end devices to the network server as UDP/IP packets. The network server, connected to the gateways via Ethernet (802.3), manages the transmission of downlink packets and sends control signals to the end devices, which the gateways convert and transmit over the LoRaWAN radio channel. Further communication is established with application servers, which may be operated by third-party organizations, enabling a single network server to support multiple application layers [44]. The LoRaWAN architecture is depicted in Figure 1 below.  
LoRa is an affordable wireless modulation technique with a long range capabilities, easily integrable into a network, and utilizing linear spread spectrum technology on unlicensed frequencies [45]. It decreases interference through forward error correction (FEC) and requires minimal infrastructure.  
LoRa has been successfully tested indoors, demonstrating stable connectivity even in the presence of multiple obstacles. In [46], the authors evaluated LoRa's performance, finding that at short ranges, the RSSI is high when the propagation factor is low. However, at greater distances from the gateway, interference increases significantly, necessitating the cooperation of end devices with higher propagation factors. In [47], extensive experiments were conducted with various spreading factor (SF) values. The findings indicate that SF = 7 is optimal for indoor localization using LoRa at short ranges, as it enables more accurate RSSI measurements while providing a higher data rate and lower latency compared to higher SF values like SF = 12, which extend range but compromise positioning accuracy.  
Based on previous studies, the following LoRa parameters are optimal for signal transmission in small indoor spaces: a coding rate of CR = 4/5, a spreading factor of SF = 7,  
Figure 1: The LoRaWAN network architecture.  
a bandwidth of BW = 125 kHz, and a frequency of 868 MHz. These settings ensure high accuracy and efficient data transmission speed for indoor localization.
## 6 Recommended Solutions
### Peak Shaving and Load Levelling  
The management of the electricity grid is very complex, since the power consumption by the users is characterised by very marked fluctuations. Peak production by RES like solar panels does not correspond with the peak loads of the system, so storing that energy in the electrical vehicles would be very good for guaranteeing system adequacy, reducing the need of investments in the generation sector.  
Besides, the stability of the system might be endangered due to high percentage of uncontrollable loads. The use of an electrical energy storage system according to the load levelling logic consists in the storage of energy during the low load hours of the electrical system and its subsequent supply during periods of high demand from users as simulated in the QDSL battery model. This operating criterion entails above all technical benefits, in particular by optimising the operation of the thermal plants and the management of the electricity network owing to the peak shaving operation, useful for levelling the peaks of maximum electricity demand. In fact, the intervention of a storage system can satisfy the peak demand while maintaining a more homogeneous generation profile. Thermal plants, in particular those that exploit low-cost primary sources, are not designed to operate at partial load with good efficiencies. By accumulating the surplus of electricity that they generate during periods of low demand, it is possible to allow these systems to always operate near nominal load and efficiency The stored energy can be subsequently supplied during the hours of maximum demand by reducing the load of expensive peaking systems powered by natural gas.  
Higher penetration of EVs means higher electrical energy requirements, as well as bigger stress on the electrical grid. The storage systems can intervene, as previously described, to level the load curve and manage the periods of maximum demand by postponing investments on new network infrastructures and on increasing installed power. This operating strategy also favours the practice of energy price arbitrage. It takes advantage of the price difference of electricity that occurs between two times of the day or week to make an economic profit for the storage system. Electricity is purchased and stored when it costs little due to the low demand from users, and then it is resold during the hours when prices are higher.  
From an economic point of view, it is possible to affirm that energy storage systems favour the reduction and stabilization of prices in the electricity market because they release electricity production from speculation and the volatility of prices linked to fossil fuels.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 2 Formulation
### Sparse Prior  
For effective model selection, we introduce a sparse prior over the coefficients of the basis functions [28]. The sparse prior in the proposed model is based on automatic relevance detection (ARD) [28]. ARD evaluates the importance of a feature with a precision parameter estimated from the data. The model uses \(\{\alpha_{j}\}_{j=1}^{J}\) and \(\{\beta_{k}\}_{k=1}^{K}\) for the numbers of components and basis functions, respectively, while \(\eta\) signifies the overall magnitude of the mean coefficients:  
\[\tilde{Z}_{k} \sim\mathcal{N}(0,\eta^{-1}\beta_{k}^{-1}),\forall k=1:K \tag{3}\] \[W_{jk} \sim\mathcal{N}(0,\alpha_{j}^{-1}\beta_{k}^{-1}),\forall j=1:J,k= 1:K \tag{4}\]In the model, \(\alpha_{j},\beta_{k},\eta,\sigma^{-2}\) are all variables of precision parameters, coming naturally with a conjugate prior of Gamma distribution that facilitates efficient posterior optimization. The probabilistic graphical model is depicted in Figure 1. Setting \(a_{0},b_{0}\) to a small value yields a vague Gamma prior that approximates a noninformative (Jeffreys-type) prior.
## 3 Results
### Robustness Analysis  
**External Validation.** Due to variations between different datasets, a model may perform well on the training dataset but not generalize effectively to external data. To further assess the robustness of the proposed method in this paper, we utilized STU [37] as an external dataset to evaluate models trained on Dataset B [21]. As demonstrated in Table 3, our method outperforms others on three evaluation metrics in the external validation dataset. Compared to the second-best results, these metrics show improvements of 1.38%, 2.06%, and 0.39%, respectively. These findings suggest that our method exhibits insensitivity to input data variations and has good generalization capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**All**} & \multicolumn{3}{c}{**Benign**} & \multicolumn{3}{c}{**Malignant**} \\ \cline{2-10}
**Method** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** \\ \hline UNet [18] & 0.7339 & 0.6462 & 0.0251 & 0.7496 & 0.6581 & 0.0174 & 0.7034 & 0.6233 & 0.0401 \\ Attention U-Net [32] & 0.7407 & 0.6590 & 0.0231 & 0.7594 & 0.6786 & 0.0164 & 0.7045 & 0.6211 & 0.0362 \\ UNet++ [1] & 0.7345 & 0.6493 & 0.0210 & 0.7596 & 0.6776 & 0.0141 & 0.6858 & 0.5945 & 0.0345 \\ UNet3+ [2] & 0.6769 & 0.5944 & 0.0247 & 0.6634 & 0.5798 & 0.0213 & 0.7031 & 0.6227 & 0.0313 \\ PraNet [23] & 0.7681 & 0.6714 & 0.0178 & 0.8008 & 0.7010 & 0.0100 & 0.7047 & 0.6140 & 0.0331 \\ DoubleU-Net [33] & 0.7672 & 0.6765 & 0.0192 & 0.7933 & 0.7016 & 0.0118 & 0.7164 & 0.6277 & 0.0333 \\ UACANet [25] & 0.7600 & 0.6711 & 0.0185 & 0.7917 & 0.7044 & 0.0109 & 0.6984 & 0.6065 & 0.0331 \\ SANet [34] & 0.7535 & 0.6722 & 0.0195 & 0.7726 & 0.6899 & 0.0118 & 0.7165 & 0.6378 & 0.0344 \\ UNext [35] & 0.6948 & 0.5984 & 0.0208 & 0.6966 & 0.5986 & 0.0139 & 0.6912 & 0.5982 & 0.0343 \\ CaraNet [36] & 0.7742 & 0.6971 & 0.0171 & 0.8051 & 0.7300 & 0.0096 & 0.7144 & 0.6331 & 0.0315 \\ AAU-Net [4] & 0.7814 & 0.6910 & – & - & – & – & – & – & – \\ DuAT [11] & 0.8046 & 0.7219 & 0.0161 & 0.8464 & 0.7616 & 0.0085 & 0.7233 & 0.6449 & 0.0308 \\ PVT-CASCADE [10] & 0.8119 & 0.7321 & 0.0180 & 0.8586 & 0.7819 & 0.0099 & 0.7213 & 0.6355 & 0.0338 \\ NSBR-Net (Ours) & 0.8148 & 0.7308 & 0.0174 & 0.8431 & 0.7624 & 0.0100 & 0.7600 & 0.6694 & 0.0317 \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of different methods on Dataset B [21] to validate our model’s learning ability. \(\uparrow\) denotes the higher the better, and \(\downarrow\) denotes the lower the better. Red indicates the best results and blue represents the second-best results.  
Figure 6: Qualitative comparison of different methods on BUSI [19] (first row and second row) and Dataset B [21] (third row and fourth row). The red curve is the ground truth boundary. The green curve is the segmentation results of these methods.