esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
In recent years, research on the effect of KTX on urban development has paid more attention to population mobility, regional economic development, and the imbalance of regional development. However, little has focused on the straw effect of KTX. The main purpose of this study was to examine the straw effect of new KTX operation through constructing the indicator of interregional dependency, which is capable of reflecting whether the population became concentrated in Seoul or spread to other parts of Korea during the evolution of KTX. Regarding this question, this study used a panel data model to conduct the empirical analysis. Overall, the larger the economic scale of the city of interest, the lower the level of dependency on other cities. The regression results confirm that the KTX connection will increase other cities' dependency upon Seoul; however, on the contrary, this effect is not significant with regard to the other four cities, which means the straw effect of KTX connection exists in terms of Seoul. Considering the robustness of our results, we introduced market access as an alternative independent variable, the regression result is consistent with our baseline results. The potential contribution of this study is outlined below. First, we constructed an indicator of interregional dependency to reflect one city's level of dependency on another before and after the operation of KTX. Second, we established a more real-acts equation than the cross-section data model through an individual time-fixed panel data model, which integrated the information of individual and time to examine the effect of new KTX developments on interregional dependency. Third, in order to avoid the interference of lurking variables, we also controlled city characteristic variables and introduced market access as an alternative independent variable for the consideration of robustness. Moreover, the empirical results pertaining to the influence of new transport infrastructures on socio-economic development could be a guidance of business development around high-speed railway stations and urban planning in the future. The results in this study underscore the importance of having a comprehensive assessment on the costs and benefits of new transport projects, particularly taking regional disparity, in terms of socio-economic development, into account. We anticipate that a failure to do so could potentially aggravate regional inequality in the long run, as well as lower the overall welfare level of society. Since the existing literature has not reached conclusive results on the HSR-regional economic development nexus (i.e., straw effect or sprawl effect) theoretically or empirically. Future research into this area could develop more reasonable dynamic models of the joint evolution of HSR constructions and regional economic development. Moreover, with the introduction of HSR in more countries worldwide, future study could replicate our results in other contexts and unpack the specific channel through which new HSR constructions affect local relative attraction. As for those large or federal
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 2 Demand Side Management
### Implementation of DLC Algorithm  
In the DLC method, DSM is implemented through the action of partial load curtailment. The goal of DLC is to reduce the power consumed when high consumption is experienced by the user. The flowchart in Figure 3 measures the power of each load. It starts by comparing each load with its pre-declared threshold (P\({}_{\text{TH}}\)) during an interval of (\(\Delta\)t) 30 min. Once the consumed power by the loads/users exceeds this threshold, load control is taken into place. DLC curtails 10-30% of the load, maintaining the loads with the highest priority active. Finally, if consumption drops below P\({}_{\text{TH}}\), the algorithm automatically connects the curtailed percentage back to the load.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
## 4 Data Preparation
### Data Cleansing and Pre-Processing  
The collected datasets are merged based on the date and the hour of the day. Only the data from the time span of 1 January 2017, 00:00 to 30 December 2018, 23:00 are utilized. Data cleansing techniques are applied to ensure the quality of the data. Duplicate rows are dropped, categorical variables are converted to numerical representations, and every measurement is converted to a floating-point value. In addition, the missing values are substituted with a global constant. Mean imputation is performed to address cases in which the measurements from the weather dataset are not available at a particular timestamp. Mean imputation replaces all missing values with a mean value calculated across the whole dataset. After data cleansing, since the data for some hours are missing from the official API, 16,566 h worth of data are available. The dataset is arbitrarily split into two parts. The earlier 90% of the data are used for training, and the later 10% are utilized for testing purposes. Then, the input data are normalized using a Min-Max scaler. As a result, each feature is converted into a \(\{0,1\}\) range. The Random Forest model is employed to select features. The data collected are extensive and combine three different datasets. Consequently, it is important to showcase which features contribute to prediction and which are insignificant. Moreover, feature selection ensures that features that do not affect the prediction are removed and do not introduce extra noise into the system.
# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU
## 3 Results
### Experimental Results
By comparing and analyzing the error results of different algorithms, the accuracy improvement achieved by the proposed MCC algorithm in three different error types is shown in Figure 12a. The maximum improvement in accuracy reaches 73%, while the minimum is 13%. In addition, Figure 12b shows the starting-end point distances of the trajectories obtained by each algorithm. Through comparative analysis, it can be observed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Foot** & **Mean Error** & **Maximum Error (ME)** & **RMSE** \\ \hline Traditional & Left & 0.13 & 0.71 & 0.19 \\ ZUPT & Right & 0.30 & 1.36 & 0.41 \\ \hline ZC & Left & 0.10 & 0.56 & 0.15 \\  & Right & 0.30 & 1.26 & 0.41 \\ \hline ZC-SC & Left & 0.13 & 0.28 & 0.15 \\  & Right & 0.16 & 0.66 & 0.21 \\ \hline ZC-DC & Left & 0.08 & 0.23 & 0.11 \\  & Right & 0.17 & 0.70 & 0.23 \\ \hline MCC & Left & 0.  
that the MCC algorithm also exhibits a significant improvement in the convergence of the trajectory endpoints. By comparing and analyzing the results of multiple algorithms mentioned above, a deeper understanding of the characteristics and functions of different algorithms can be obtained. For example, by comparing Figure 11c,e, it is evident that the single-foot constraint has a significant correction effect, especially in suppressing long-term heading divergence and closing point convergence of a single IMU. However, the single-foot constraint fails to effectively maintain the consistency of the dual-feet trajectory, leading to a noticeable discrepancy with the actual walking trajectory. Further comparison of Figure 11e,g shows that the dual-feet constraint outperforms the single-foot constraint in maintaining the consistency of the dual-feet trajectories. However, in terms of heading calibration for multi-loop trajectories, the dual-feet constraint algorithm is slightly inferior to the single-foot constraint. Although the dual-feet constraint algorithm can bring the trajectories of the two feet closer together, it may, to some extent, affect the stability of the heading. Therefore, combining the single-foot and dual-feet constraints can complement each other's weaknesses. In the integrated algorithm, both their advantages and disadvantages are harmonized. Furthermore, this approach does not require additional auxiliary equipment, achieving a more optimal overall result. Overall, the multi-condition constraint algorithm demonstrates superior performance in both adaptability and stability.
## 3 SDR Technology
### SDR Architecture  
Nowadays, various Commercial-Off-The-Shelf (COTS) SDRs are available for use. These SDRs vary in terms of cost and operational modes, i.e., transmitting, receiving or duplex modes, operating bandwidth, frequency bands, and the amount of coherent RF channels. Table 2 provides a brief comparison among several commercial SDR front-end units. In the following sections, the reception and transmission operations that are handled by the SDR front-end are briefly introduced.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 4 Experimental Analysis
### Experiment Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture Name** & **Description** \\ \hline NextDet & The proposed NextDet architecture in this paper. \\ \hline NextDet + FPN & NextDet architecture with FPN network only. \\ \hline NextDet-SAM & NextDet architecture without spatial attention modules. \\ \hline ShuffDet + PAN & NextDet architecture with ShuffleNet backbone and PAN network. \\ \hline ShuffDet + FPN & NextDet architecture with ShuffleNet backbone and FPN network. \\ \hline Mob3 Det + PAN & NextDet architecture with MobileNetv3 backbone and PAN network. \\ \hline Mob3 Det + FPN & NextDet architecture with MobileNetv3 backbone and FPN network. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of acronyms of different versions of object detection architectures utilized in training and analysis phase. Extrapolating benchmarking results from Tables 2 and 3, it can be observed that the proposed object detector, NextDet, achieves a notable performance in Argoverse's monocular streaming perception as well as general-purpose multiclass object detection in Microsoft COCO datasets. Furthermore, it can be observed that eliminating certain strategies and techniques such as attention modules, results in degradation of performance of NextDet. Likewise, replacing the ultra-efficient backbone, CondenseNeXt, with other popular lightweight CNNs such as MobileNetv3 and ShuffleNet, degrades NextDet's object detection performance significantly, and appositely corresponds to performance metrics in Table 4. Thus, the experiments conclude that the backbone and head of an object detector play a crucial role in boosting an object detector's detection performance than the neck because backbone emphasizes on locating an object in an input image, and the head focus on detecting and refining the location of the bounding boxes. The proposed NextDet object detector is a combination of such novel and modified techniques and strategies in the backbone, neck and head which results in a notable performance benchmarked in Tables 2 and 3. In addition, Figure 9 provides a heatmap of class activation mapping of an
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 5 Biological Functions of RAC1B
### Proliferation/Cell Cycle Regulation  
One of the best studied responses to RAC1B activation in tumor cells is proliferation and cell cycle progression. In mouse fibroblasts expression of wild-type RAC1B was sufficient to stimulate CYCLIN D1 accumulation and G1/S progression, and both responses were blocked by the NFxB super-repressor \(\mathrm{IkB\alpha(A32A36)}\)[41]. Similarly, in colorectal cells depletion of RAC1B by siRNAs was associated with a reduced G1/S progression rate and inhibited endogenous NFxB activation [42]. In another cell model, HEK293T cells stably overexpressing RAC1 or RAC1B, the level of CYCLIN D1 was significantly increased in both serum-starved RAC1 and RAC1B overexpressing cells. RAC1B overexpression also stimulated G1/S progression and induced a significant increase in _CCND1_ reporter activity in the K1 PTC-derived thyroid cell line [25]. In HEK293T and SW480 cells stably overexpressing RAC1B, the pro-proliferation genes _MAPK8_ (encoding JNK2), _JUN_ and _CCND1_ were all upregulated and RAC1B promoted cell proliferation by activating the JNK2-c-JUN-CYCLIN D1 pathway [43] (Figure 3). RAC1B was also required for neurotrophin (NT3)-stimulated cell proliferation of human mesenchymal stem cells (hMSCs) [44].  
Furthermore, RAC1B may synergize with oncogenes to stimulate proliferation. In primary colorectal tumors and colorectal cell lines RAC1B overexpression and B-RAF-V600E are significantly associated and the simultaneous suppression of both proteins dramatically impaired cell cycle progression [45]. Moreover, synergism of RAC1B with K-Ras-G12V in a mouse model of lung adenocarcinoma enhanced proliferation and tumor growth [24]. Finally, RAC1B may indirectly stimulate proliferation by blocking the action of growth inhibitory factors. [PERSON] and coworkers have found that RAC1B acts to block growth inhibition by TGF-\(\beta\)1 in pancreatic [35] and breast cancer cells [46]. RAC1B knockdown resulted in upregulation of the cyclin-dependent kinase (cdk) inhibitor p21 WAF1 [35], a known mediator of TGF-\(\beta\)-induced growth arrest. Whether RAC1B also acts as a TGF-\(\beta\) signaling inhibitor in normal/benign precursor cells, thereby compromising the tumor suppressor activity of TGF-\(\beta\) and eventually promoting malignant transformation (Figure 3) remains an interesting issue for future research.  
While the majority of studies reported a proliferation-promoting role of RAC1B under conditions of ectopic overexpression, others made observations that do not support such a role. For instance, in NIH3T3 cells, Rac1b, unlike activated Rac1-Q61L, did not stimulate transcription from the _CCND1_ promoter [7]. Rac1b was also not required for K-Ras-driven cell proliferation [24]. In Madin-Darby Canine Kidney cysts, a model for polarized epithelial structure, expression of Rac1b, unlike wild-type Rac1 or Rac1-G12V, did not promote cell cycle progression, leading the authors to conclude that the expression of Rac1b per se cannot induce cell proliferation [47].
## 3 Results and Discussion
### Slicing Check Characteristics  
The mean check depth ratio per studied thickness group and examples of the slicing checks' appearance are presented in Figure 4. The check depth ratio decreased with the increasing sliced lamellae thickness, i.e., 68% for the 1.5 mm thick sliced lamellae to 50% for the 4.5 mm thick sliced lamellae. The results indicate a negative correlation between check depth ratio and the strength properties, MOR, and strain at break (Figure 4).  
Extensive slicing checks when slicing oak species may be mainly attributed to the presence of multiseriate rays that constitute stress-concentration areas during slicing [4,14]. Additionally, large earlywood vessels constituting regions of low mechanical properties facilitate crack propagation [40,43]. Overall inefficiencies in oak species wood plasticisation due to anatomical characteristics, e.g., tyloses [15], may also contribute to extensive slicing checks. If plain-sliced oak lamellae are used for structural applications, the middle range  
Figure 4: Example of slicing checks highlighted by a stain for (**A**) 1.5 mm, (**B**) 2.5 mm, (**C**) 3.5 mm, and (**D**) 4.5 mm thick oak sliced lamellae (left); mean check depth ratio for the evaluated SL thicknesses (upper right) and mean MOR and strain at break as functions of the check depth ratio (lower right).  
Figure 3: Examples of most common stress–strain curves observed on oak sliced lamellae; elastic followed by plastic deformation (**left**) and elastic with plastic strain mid-deformation (**right**).  
of the selected thickness groups could be beneficial, i.e., 2.5-3.5 mm. Lamellae slicing at 4.5 mm and above would provide only marginal benefits in terms of yield and productivity when compared to sawn solid wood while the decreasing material mechanical properties significantly.  
Hence, the use of oak sliced lamellae for structural applications is rather unlike; therefore, the current study's results could benefit a more common use for decorative products such as flooring and furniture panel production. Understanding a raw material's most critical mechanical properties can facilitate further testing method development and product optimisation. Further studies should ascertain the impact of sliced lamellae thickness on surface checking in sliced-lamellae-based finished products. A possible impact of sliced lamellae thickness on surface checking could enable the use of mechanical properties, e.g., MOR and strain at break, as predictors for the defect occurrence. The benefit of using mechanical properties as a quality control method is due to the potential correlation between static and dynamic methods and the use of non-destructive means for the latter.
## 3 Experimental Setup and Results
### Results from Majority Voting Experiment  
In this experiment, we used the same combinations of the Base-Level classifiers in the proposed stacking approach to compare the results in the two approaches. As shown in Table 9, the results achieved by this approach are not suitable as the ones achieved by the proposed stacking approach. The improvements of the proposed stacking approach over the majority voting are 1.44%, 1.61%, 0.55%, and 1.71% on HatEval, Davidson, COVID-HATE, and ZeerakW datasets, respectively.  
It can be seen in Table 9 that among the majority voting ensembles (LR, NB, RF) consistently has the lowest performance for all datasets. RF is an ensemble method consisting of tree predictions; these predictions are provided by majority voting of each tree ([PERSON] et al., 2019). In the (LR, NB, RF) combination, in most cases, RF agrees with LR or NB or both in wrong class predictions leading to the misclassification in the majority voting approach. Moreover, RF performs better with multiclassification tasks.  
The majority voting ensemble containing SVM, LR, and E-tree provide the highest F1-score on both HatEval and Davidson datasets. The best F1-score on the COVID-HATE dataset is achieved with the (KNN, LR, NB) combination. Finally, for the ZeerakW dataset, the majority voting ensemble using the (SVM, LR, XGB) combination achieved the highest F1-score.
## 2 System Model
### Local Spectrum Sensing Model  
We denote by \(\mathcal{H}_{1}\) and \(\mathcal{H}_{0}\) the binary hypotheses of the presence and absence of the PU on the uplink channel, respectively. Without loss of generality, we choose the \(m\)-th SSN from the three-tier structure of sensor network to describe its local spectrum sensing model during the slot \(\tau_{s}\). This formulation can be easily extendable to the general case for any SSN from one of the tiers including \(\Pi\), MT, and ET. The sampled signals that are received at the \(m\)-th SSN on the \(k\)-th uplink channel during the slot of spectrum sensing \(\tau_{s}\) are given as:  
\[y_{m,k}(u)=\left\{\begin{array}{ll}h_{m,k}s(u)+v_{m,k}(u),&\mathcal{H}_{1}\\ v_{m,k}(u),&\mathcal{H}_{0}\end{array}\right. \tag{6}\]  
where \(s(u)\) denotes the signal from the PU on the \(k\)-th uplink channel with a sampling frequency \(f_{s}\), \(v_{m,k}(u)\) is the noise at the \(m\)-th SSN on the \(k\)-th uplink channel, \(h_{m,k}\) is the channel gain between the PU and the \(m\)-th SSN on the \(k\)-th uplink channel implying Rayleigh fading. Then the number of samples that is collected by the \(m\)-th SSN on the \(k\)-th uplink channel can be defined as \(U_{s}=f_{s}\tau_{s}\). We assume that the PU signal \(s(u)\) satisfies an independent identically distributed (_i.i.d._) random process with zero mean and variance \(\sigma_{s}^{2}\), and the noise \(v_{m,k}(u)\) is _i.i.d._ circularly symmetric complex Gaussian with zero mean and variance \(\sigma_{0}^{2}\)[8]. Thus, the received SNR from the PU at the \(m\)-th SSN on the \(k\)-thuplink channel is given by \(\gamma_{m,k}=\left|h_{mk}\right|^{2}\sigma_{s}^{2}/\sigma_{v}^{2}\). After collecting \(U_{s}\) received signal samples on the \(k\)-th uplink channel, the \(m\)-th SSN obtains its test statistics given as \(Y(m,k)=(1/U_{s})\sum_{n=1}^{U_{s}}\left|v_{m,k}(u)\right|^{2}\). Let \(\varepsilon_{m}\) denote a decision threshold by the \(m\)-th SSN to decide whether the channel is occupied by the PU. For the \(m\)-th SSN, the probabilities of detection and false alarm on the \(k\)-th uplink channel are approximately formulated as follows [8]:  
\[p_{d}(m,k)=\Pr(Y(m,k)>\varepsilon_{m}|\mathcal{H}_{1})=\mathcal{Q}\Bigg{(} \bigg{(}\frac{\varepsilon_{m}}{\sigma_{v}^{2}}-\gamma_{m,k}-1\bigg{)}\sqrt{ \frac{U_{s}}{2\gamma_{m,k}+1}}\Bigg{)} \tag{7}\]  
\[p_{f}(m,k)=\Pr(Y(m,k)>\varepsilon_{m}|\mathcal{H}_{0})=\mathcal{Q}\Big{(}( \varepsilon_{m}-1)\sqrt{U_{s}}\Big{)} \tag{8}\]  
where \(\mathcal{Q}(\cdot)\) denotes the right-tail probability of a normalized Gaussian distribution. Hence, the detection probability set \(\mathcal{P}_{d}\) and the false alarm probability set \(\mathcal{P}_{f}\) for the \(m\)-th SSN over the entire uplink channels can be further expressed as:  
\[\mathcal{P}_{d}=\left\{p_{d}(m,1),p_{d}(m,2),\cdots,p_{d}(m,k),\cdots,p_{d}(m, N_{c})\right\} \tag{9}\]  
\[\mathcal{P}_{f}=\left\{p_{f}(m,1),p_{f}(m,2),\cdots,p_{f}(m,k),\cdots,p_{f}(m, N_{c})\right\} \tag{10}\]
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 3 Results and Discussion
### Influence of microalgae-Bacteria Inoculation Ratio on Nitrogen and Phosphorus Removal
[26], these results can be related to the low symbiosis between microalgae and bacteria for P removal by the two pathways. Thus, the P concentration was two to three times greater than that of total N, but PBRs with the highest inoculation ratio showed that P removal was above 80%. [PERSON] et al. [27], using domestic wastewater treatment and similar photobioreactor operation for 14 days, reported P removal efficiency of 54% for PBR with only microalgae inoculation and 10.6% for PBR with only activated sludge inoculation. The authors hypothesize that the inhibition of light between algal cells in the first condition resulted in limited autotrophic growth in the medium. The process of growth and photosynthesis of the microalgae was also affected without the additional supply of CO2 provided by the bacteria (sludge). The low aggregation process of bacteria with microalgae, which may also suggest the aerobic sludge's low P removal capacity, may have influenced PBR with bacteria only. Additionally, the absence of anaerobic and anoxic phases in this system allows for P-accumulating organisms to remove the waste (PAOs) [49; 50]. Table 2 shows RE for N and P for different studies in photobioreactors. [PERSON] [14] and [PERSON] et al. [51], using the species _Scenedesmus_ sp. and _Chlorella vulgaris_, obtained removals of NH4\({}^{-}\)-N and PO4\({}^{3-}\)-P above 90%. In contrast, [PERSON] et al. [52] and [PERSON] et al. [53], treating domestic wastewater with microalgae inoculation, reported NH4\({}^{-}\)-N RE between 94% and 98%, but a low P removal. [PERSON] et al. [54], using a 1:3 algae/bacteria ratio for treating industrial dyes wastewater, with an initial VSS concentration in the mixed liquor of 3000 mg/L, achieved an 83% RE for NH4\({}^{-}\)-N, but only around 30.2% for P removal. It is worth noting that the initial NH4\({}^{-}\)-N concentration was 20 mg/L to 30 mg/L and the P concentration was 5 mg/L to 6.5 mg/L. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Influent** & **MA** & **AS** & **NH4\({}^{-}\)-N (\%)** & **PO4\({}^{3-}\)-P (\%)** & **Reference** \\ \hline Paper industry & Yes & No * & 96.0 & 91.0 & [14] \\ Paper and cellulose & Yes & No * & 99.0 & 97.0 & [51] \\ industry & Yes & Yes & 98.4 & 34.7 & [53] \\ Domestic & Yes & Yes & 94.0 & 46.0 & [52] \\ Dye industry & Yes & Yes & 84.9 & 30.2 & [54] \\ Paper pulp industry & Yes & Yes & 86.8 & 85.8 & This study \\ \hline \end{tabular}  
* Only microalgae inoculation was studied in the cited papers. No studies were found with microalgae-bacteria consortium applied to paper pulp effluents.  
\end{table} 
Table 2: Comparison of NH4\({}^{-}\)–N and PO4\({}^{3-}\)–P removal efficiencies in photobioreactors with various types of wastewaters.