esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Classification
#### 3.3.2 Random Forest  
The Random Forest (RF) algorithm and the DT algorithm are similar. RF consists of many individual DTs. RF is an ensemble learning method and is known for one of the most widely used algorithms for solving multi-classification and prediction tasks. It combines random subspace and bagging ensemble learning. Bagging is a bootstrap aggregation method that uses a simple random sample with replacement for imbalanced datasets. In bagging, and as RF's name suggests, it follows a random mechanism when generating results. Unlike decision trees, RF provides a fixed-size subset of all the attributes; a tree of all properties is not generated at all times. This significantly reduced the computational cost [22]. The conditional characteristics of a RF are determined individually by each tree. When a sample reaches a root node, it is propagated to all sub-trees. Each sub-tree anticipates the class label for that specific sample. Finally, the highest class is assigned to that sample. The main procedure of RF includes the following steps  
Step 1: The sample is first randomly selected from a dataset using the simple random sampling bootstrapping resampling approach.  
Step 2: A selection of attributes from all the attributes in the dataset will be generates a random subspace.  
Step 3: Using bootstrap sampling, a tree is constructed using the ID3 algorithm and performs a voting mechanism on each predicted result.  
Step 4: Repeat steps 1-3 until it generates a sufficient number of trees to reach the desired result, and then select the prediction with the highest votes as the final prediction.  
Suppose \(S\) is a dataset with \(X=\{x_{1},\ x_{2},\ \ldots,\ x_{n}\}\) features and a target class label \(y\). This method is a standard representation of the random forest. When training a group of classifiers, RF is expressed by the following equation.  
\[D=\{(x_{i},y_{i})\}^{n}i=1 \tag{3}\]  
The Gini index is used to assess the quality of the split. The node's \(GI\) is determined by  
\[GI(N)=\sum_{i\
eq j}P(\omega_{i})P(\omega_{j}) \tag{4}\]  
where \(P(i)\) indicates the ratio of entities in the dataset for a class category \(i\).
## 3 Results and Discussion
### Modeling the Color Appearance of Carpets
For carpet features, pile height has the most significant impact compared to stitch rate and gauge. For red-colored carpets, the effects of carpet feature over L*, a*, and b* values were more prominent. Pile height and stitch rate were found to be negatively affecting the values in all illuminants, where pile height has the most significant impact compared to stitch rate. Negative coefficients refer to the inverse relationship with the dependent variables. This means that the lower the pile height and stitch rate, higher the availability of yarn in the horizontal loop-arch plane, which in turn increases the values of L*, a* and b*. For green-colored carpets, only dye concentration was found to be significantly affecting the L* and a* values, except for the fluorescent illuminant. Carpet features become prominent in the F02 illuminant. This could be the higher relative power of the green wavelength in F02 illuminants' spectra. The differences in MLR models for red and green colored carpets can again be associated with the corresponding dye/colorant molecules' energy absorption/reflection pattern. The nondominant hue measured via the b* values were found to be affected by all the carpet features, positively by gauge and negatively by stitch rate and pile height. This again refers to the availability of more yarn in the horizontal loop-arch plane. In summary of the MLR modeling, it can be inferred that the apparent color of the loop-pile carpet is mostly affected by the colorant concentration on the loop-arch yarn. Moreover, pile height, stitch rate and gauge determine the availability of loop-arch yarn on the carpet pile surface, which could significantly affect the light reflection and subsequent color perception.
## 2 Materials and Methods
### Preliminary Sensory Evaluation  
In the first stage of the research, sensory analyses were carried out in order to select the most acceptable concentration of yellow tea extract addition to chocolate. Commercially available white chocolate callets were used as a carrier due to their low aroma and light color compared to chocolates containing cocoa liquor. A total of seven concentrations of tea extract used for different features were: 0.0%, 0.1%, 0.2%, 0.5%, 1.0%, 1.5%, and 2.0% according to chocolate dry mass. These selected samples were prepared by adding the respective mass of tea extract to 100 g of white chocolate (Belcolade, Belgium). The chocolate was tempered using a chocolate melting pot (Hendi, Poland) while stirring until  
Figure 1: Scheme of the conducted research.  
it reached 40-42 \({}^{\circ}\)C, then the powdered tea extract was added and thoroughly mixed until a smooth and homogeneous consistency. Then, enriched chocolate was poured into round molds and kept in a cooler at 4 \({}^{\circ}\)C for 20 min to solidify. Consumer assessment of aroma, color, taste, texture and overall acceptability was carried out in the sensory evaluation laboratory [48]. The developed chocolate samples were evaluated in a focus group (\(n=10\) of sensory trained professionals from The University of Life Sciences in Poznan) to select the most acceptable concentration of tea extract based on sensory descriptors intensity [49]. Participants were given two pieces of coded samples of chocolate from each extract's concentration and were asked initially to identify the intensity of taste. Evaluation was conducted under red light in order to mask color differences between food samples and avoid sample color suggestion. Once identified, they had to rate the sensory evaluation criteria of aroma, color, taste, texture and overall acceptability on a 10-point hedonic scale with boundary indications: \"I do not like it extremely\" (1) to \"I like it extremely\" (10). Finally, the most liked samples out of seven concentrations for yellow tea extract were selected for further analyses. The data were expressed as the medians of all the scores.
## 3 Results and Discussion
### Elderberry Stalks Mineral Composition  
The essential mineral content of elderberry stalks, assessed by ICP-AES, is listed on Table 1. The dietary reference intake (DRI) recommended dosage per day is also presented for comparative purposes.  
Except for sodium, the minerals content of elderberry stalks ranged from 25 to 100% of the DRI, suggesting that elderberry stalks could be used as a natural source of these essential minerals. In fact, the ingestion of a portion of 100 g (dw) would contribute to the dietary reference intake of minerals such as calcium, iron, potassium, magnesium and zinc [41]. These minerals are associated with the body function, for instance, in hemoglobin production (iron), in keeping the blood pressure stable (calcium) and in the regulation of the osmotic balance and muscles activity (potassium). Elderberry stalks showed similar contents of Mg when compared with stalks from different grape cultivars, while the other minerals were present in lower amounts [42].  
The ICP-AES analysis was also used to search for lead due to its possible toxicity [43], and the results allowed to verify the lead content on elderberry stalks was detected with a concentration of \(<\)0.1 mg 100 g\({}^{-1}\) dw (limit of detection of the methodology). Although, for these samples, the lead content does not represent a concern, it is important to control this element in this type of waste as its content is related to several parameters associated with environmental issues, agricultural practices, among others.
## 5 Numerical Comparison of the Existing Mass-Rig Systems
### Numerical Modeling of the Inertial Systems
#### 5.1.3 Model III  
The third IMS considered to be compared to Model I is commonly termed the mass-rig. In this setup used by [PERSON] et al. [15], the additional inertial mass for the shake table experiments is placed on a horizontally constraint-free system located outside the shake table where only the column specimen is subjected to dynamic loading. To create Model III in OpenSees, a minor change must be introduced to Model I to replicate the mass-rig system, although these modifications do not affect the column modeling strategy used. The modification consists of applying earthquake loading only to the column model because in the test setup proposed by [PERSON] and shown in Figure 4, only the column model is fixed to the shaking platform.  
Figure 14: Model II.  
It is worth noting that, according to [PERSON] [15], in addition to the inertial forces caused by the total mass on the mass-rig, significant lateral forces were transmitted to the specimens due to secondary moments (P-\(\Delta\) effects) on the mass-rig. These additional lateral forces resulted from the overturning moment of the system, which is equivalent to the vertical force times the lateral drift. As was also pointed out by [PERSON], no considerable secondary moments were generated in the column specimen due to the axial force system. As in Model I, to represent the additional forces transferred to the column, the inertial system was modeled using an elastic beam-column element with a pin support and a total height equal to the vertical pin-to-pin distance of the IMS (243.8 cm). Additionally, P-\(\Delta\) effects in the IMS were considered using the option of corotational geometric transformation. Figure 15 illustrates the numerical model used for unidirectional tested specimens.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Proposed Sparse 2D FIR Multibeam Beamformer
Here, \(s_{out}(n_{x},n_{ct})\) is computed for \(-(N_{x}-1)/2\leq n_{x}\leq(N_{x}-1)/2\) and \(0\leq n_{ct}\leq(N_{ct}-1)\), where \(N_{x}\) and \(N_{ct}\) are the number of broadband antennas in the ULA and the number of temporal samples in \(w(n_{ct})\), respectively. The design of \(H(z_{x},z_{ct})\) with sparse coefficients is presented next.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Mechanisms for Spatial Reuse in Wi-Fi 6
#### 5.3.2 Intra- and Inter-BSS NAV  
The standard proposes that a station utilizes distinct NAVs for different BSSs. A station updates its NAV value independently for each BSS color whenever a NAV value is read in a frame and when a Cf-end frame is received. A station can transmit a frame only if _all_ its NAV values are equal to zero, preventing collisions. However, most devices and research papers approximate BSS-specific NAVs with one intra-BSS NAV and one inter-BSS NAV [113].  
Figure 13: OBSS PD: A station’s inter-BSS NAV is updated only if the RSSI of an inter-BSS packet is larger than the \(\mathit{OBSS\_PD}_{\mathit{level}}\). Otherwise, the station can perform a concurrent transmission with a transmission power that can be derived from this diagram when the \(\mathit{OBSS\_PD}_{\mathit{level}}\) is given.
## 2 DBQW RTD Photo-Detectors  
Double barrier quantum well RTD based photo-detectors are interesting alternatives for optical detection when compared with traditional photo-detectors due to their photo detection mechanism and built in electrical gain arising from RTD's current-voltage (I-V) characteristic showing a region of negative differential resistance (NDR). With optimum epi-layer structure design that takes advantage of a photo detection mechanism based on the scenario that in biased RTDs the photo-excited electron-hole pairs become locally separated, which in turn modulates the internal electric field, RTDs photo-detectors can have sensitivities of up to \(10^{3}\) A/W [21]. By taking advantage of these effects, RTD structures can be used to implement light-by-light switching and optically switched RTD photo-detectors. Early demonstrations of such optically switched devices have shown potential for high-speed transmission communications at Gb/s rates with very low electrical power dissipation [22]. Demonstration of single photon detectors were also achieved combining DBQW-RTDs and quantum dot structures [23].  
Resonant tunneling diode based photo-detectors (RTD-PDs) consist of nano-electronic structures that use vertical stacking of epitaxial layers of semiconductor alloys with the active regions consisting of 10-nm thick InGaAs/AlAs DBQW semiconductor alloys, surrounded by InGaAsAlAs photoconductive layers that act as like a Fabry-P \(\ddot{\mathrm{o}}\)rot interferometer for the electron wave functions. The Fabry-P \(\ddot{\mathrm{o}}\)rot effect gives rise to a highly nonlinear and N-shaped current _versus_ voltage characteristic which provides the occurrence of NDR. DBQW-RTD structures for photo-detection emerged in the last decades as devices with high responsivities and gain-bandwidth-efficiency products, as well as low switching energies [23] because of their intrinsic gain and low voltage operation characteristics arising from the NDR effect. Essentially, they work as follows: a light pulse incident upon a resonant tunneling structure biased close to the peak voltage produces photo-charges that reduce the series resistance, leading to a shift of the peak and valley voltages which can induce RTD switching from the peak to the valley of I-V NDR and give rise to changes in the current flow [24].
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 4 Discussion  
CHO PET is an effective molecular-level lipid metabolism evaluation approach for tumor diagnosis. PET images are recognized by limited spatial resolution, and this paper established conventional and deep learning methods for CHO PET denoising. All proposed algorithms reduced noise to a certain extent, and deep learning methods (N2N and N2V) preserved more details of the tumor region compared to conventional filters (BM3D and NLM). N2N had superior performance among all algorithms, with a higher CNR improvement ratio of 13.60% \(\pm\) 2.05%, lower entropy of 1.68 \(\pm\) 0.17 and fewer changes in tumor features (SUVmax, SUVmean and TLA). This proposed method provided clearer images for physician's diagnosis with good preservation of the representative features in tumor region, which can be expected to be applied to other low background activity radiotracer data. The conventional filters reduced noise in the spatial domain, which calculates the pixel value by weighted average of correlated pixels. Though there are algorithms, such as BM3D and NLM, to search the correlated pixels and estimate the weight of the pixels, they cause blurring of results when the noise level is high. As presented in Figure 3, the normal brain regions of conventional method results have unwanted indistinction. Deep learning-based methods, on the other hand, are data-driven and learn to remove noise from images directly instead of filtering the neighborhood pixels to smooth both noise and detail information. Although it has narrower application scenarios, a well-trained deep learning algorithm may present a better performance in executing a specific assignment. In our study, the deep learning-based networks outperformed the conventional filters with higher CNRs and CNR improvement ratios, indicating the capability of deep learning for the denoising of brain tumor CHO PET data. Previous studies are always based on FDG PET data [21], for FDG is the most widely applied radiotracer with an enormous amount of application. The injection dose of CHO is similar to the dose of FDG, but the uptake approaches of the two kinds of tracer have essential differences. FDG, as a glucose analog that can be absorbed but not further metabolized, can detail the major brain structures due to the significant glucose assumption in the cortex. CHO, on the other hand, reflects phospholipid membrane production, which is generally low in both the cortex and medulla. Therefore, CHO PET images present low background metabolism in the whole brain, that are supposed to obtain less information from brain tissue. Comparing CHO PET with FDG PET, the total uptake of radiation tracer is lower and the characteristics of the images and noise are different. Therefore, supervised denoising networks trained by FDG PET images may have difficulty
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 8 Routing in a delay-tolerant WSN to a Single Mobile Sink  
### System Model and Design Objectives  
A WSN with a mobile sink was considered in [3]. Each node can postpone data transmission until the sink is at the most favorable position to extend the lifetime of the network. The problem is to find how long the sink should stay at potential stops and how buffered data could be routed to the sink when it stops taking into account a maximum delay toleration. A distributed algorithm was used in which the problem is decomposed to smaller decision problems and each can be solved by a sensor node. Only local information from the neighbors is needed by each node.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 2 Materials and Methods
In [11], only information about the TTI for 4G was mentioned, but no information was available about the TTI considered by QualiPoc. To evaluate the TTI used by QualiPoc, a QualiPoc test during a continuous FTP upload of a huge file was performed with a 4G and a 5G connection. During these tests, a continuous UL stream of TBs at maximum capacity, i.e., one TB for each TTI, was expected to result in the maximum possible number of TBs per second. Figure 3 shows a boxplot of the average number of TBs per second for the continuous FTP upload of a huge file, calculated using Equation (1). For LTE, we observed values up to 1000 (with a few outliers, probably due to misreporting), as expected (TTI = 1 ms). For 5G, we observed values up to 3200 (99 th percentile). This did not correspond to the maximum expected value of 400 (=2000/5) for a 5G configuration with an SCS of 30 kHz (TTI = 0.5 ms) and a TDD factor of 5 (DDDSU slot configuration, i.e., 1/5 UL and about 4/5 DL). Consequently, this showed that the TTI for 5G used by QualiPoc is fixed at a duration of 62.5 us, resulting in a maximum number of TBs per second of 3200 (=16,000/5). Consequently, in the subsequent analyses, a TTI of 62.5 us was considered for 5G signals. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**4G (LTE)**} & \multicolumn{3}{c}{**5G (LTE)**} & \multicolumn{3}{c}{**5G (NR)**} \\ \cline{2-10}
**Application** & \begin{tabular}{c} **Mean (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Mean (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **ps (DC)** \\ **(\%)** \\ \end{tabular} \\ \hline Call (VoLTE) & 12.4 & 7.2 & 22.5 & - & - & - & - & - & - \\ FTP & 92.5 & 93.1 & 97.7 & 93.0 & 94.1 & 98.8 & 19.9 & 20.0 & 20.0 \\ iPerf & 93.0 & 94.1 & 97.8 & 91.3 & 93.3 & 99.1 & 17.3 & 20.0 & 20.0 \\ Ping & 56.4 & 50.2 & 98.5 & 1.0 & 0.8 & 2.1 & 1.5 & 1.5 & 1.8 \\ Twitch & 8.0 & 7.2 & 15.0 & 5.7 & 5.4 & 11.5 & 4.7 & 4.3 & 7.8 \\ WhatsApp (VoIP) & 9.9 & 5.7 & 21.2 & 0.9 & 1.0 & 1.6 & 10.4 & 10.5 & 12.6 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the duty cycles: mean, median, and 95 th percentile of the considered applications obtained from in situ measurements near a 4G and 5G base-station antenna in Leuven, Belgium. Remark that for 5G NR, there was an upper duty cycle limit of 20%, considering the used TDD slot format DDDSU.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 2 Related Work  
### Pipeline Models  
Currently, studies on intent detection and slot filling include pipeline and joint models [6]. Pipeline models refer to the separate implementation of intent detection and slot filling without establishing an association between the two tasks [1].  
Intent detection, as a text classification task, has evolved from utilizing labelled training data and training using shallow machine learning models, such as the SVM [13], improved KNN model, and MLKNN [14], to training using deep learning models including RNN [15]. IndRNN-attention [16], and Hybrid ELMo [17]. For the slot filling task, traditional approaches rely on methods such as conditional random fields (CRFs) for predicting slot labels. In recent years, neural network and their extensions [18, 19, 20, 21, 22, 23] have demonstrated superior performance in slot filling.  
However, pipeline methods model intent detection and slot filling tasks separately, ignoring the correlation between the two tasks, which can lead to error propagation problems [1]. To take advantage of the strong correlation between intent detection and slot filling, joint models have been proposed [24].
## 8 Conclusions  
Energy efficiency is an important factor in 5G and beyond networks and one of the drivers in the adoption of D2D technology. This paper has reviewed the potential application areas including IoT, wearables, and automated driving and reviewed the current status of D2D technology in 3 GPP standardization. In addition, we have analyzed D2D-enhanced cellular networks both from the base station and from the end user perspectives. The analysis is conducted with several different measurement-based LTE and WiFi models. The results show that significant energy reductions can be achieved with all types of base stations, including macro, pico, and femto base stations. The results also suggest that in order to minimize power consumption, the devices should minimize the number of active radio interfaces and use the best possible data rates. In our system model this means that either a LTE or WiFi interface is active in a single device at a given time instant. WiFi could be used to support very high data rate services. If there is no need for that, one should keep only the LTE interface active in order to save power. An interesting future topic could be to study the effect of mobility in energy consumption. This would create new challenges e.g., due to frequent handovers in a multi-RAT network. In addition, adaptive power control could be included in the analysis to gain a more detailed understanding e.g., on the effect of UL transmissions.  
This work was supported by the CORNET project, partly funded by Tekes, the Finnish Funding Agency for Innovation.  
[PERSON] performed the experiments and analyzed the data. He was also the main author of the paper. [PERSON] wrote a major part of the 3 GPP section, especially regarding the IoT and wearables. [PERSON] commented on and supported work throughout the paper.  
The authors declare no conflict of interest.