esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NMPC Formulation
### Disturbance Handling Approaches  
Three approaches for handling external weather disturbances \(d(k)\) within the NMPC framework are implemented:  
* Feedback-Only Control (NMPC-C): This approach incorporates the currently measured disturbance \(d(t)\) and assumes it remains constant over the entire prediction horizon \(N_{p}\): \(d(t+k\mid t)=d(t)\), for \(k=0,\ldots,N_{p}-1\). This represents a reactive control strategy with no foresight.
* ML-Based Preview (NMPC-P): This controller utilizes current disturbance measurements \(d(t|t)=d(t)\) for the initial control step (\(k=0\)). For the remainder of the prediction horizon (\(k=1,\ldots,N_{p}-1\)), it integrates multivariate disturbance forecasts generated by an LSTM neural network. Thus, the combined sequence matches the full NMPC prediction horizon. The LSTM leverages a sequence of recent historical disturbance measurements (defined by a lookback window of \(L\) steps) to predict the future evolution of key weather variables (\(d_{1},\ldots,d_{4}\)) over the subsequent \(N_{p}-1\) steps. At each control interval, the NMPC controller collects the most recent L measurements of all disturbance variables, feeds them to the LSTM, receives a multi-step forecast of length \(\mathrm{N_{p}}-1\), and concatenates this forecast with the current measured disturbance to form the disturbance trajectory of length \(\mathrm{N_{p}}\) used in the NMPC optimization problem:  
\[\left[\mathrm{d}(t|t),\;\mathrm{d}(t+1|t),\;\ldots,\;\mathrm{d}\big{(}t+\mathrm{ N_{p}}-1|t\big{)}\right]\]  
Here, \(\mathrm{d}(t|t)\) is the current measured disturbance, and \(\mathrm{d}(t+\mathrm{k}|t)\) (\(\mathrm{k}=1,\ldots,\mathrm{N_{p}}-1\)) are the LSTM-generated forecasts. Details on the LSTM architecture, input features, and training data are provided in Section 5.1.
* Ideal Preview (NMPC-I): This ideal scenario assumes perfect, error-free knowledge of future disturbances \(d(t+k)\) over the prediction horizon. This serves as an upper-bound benchmark for the potential benefits of disturbance prediction.  
The implementation of these strategies allows for a direct comparison of feedback, data-driven preview, and ideal disturbance information in greenhouse NMPC.
# PolySorbate-Based Drug Formulations for Brain-Targeted Drug Delivery and Anticancer Therapy
## 5 PS-Coated NPs for Efficient BBB Transport
AP-loaded PLGA NPs showed more efficient protective effects against cerebral ischemia-reperfusion injury in rats compared to the free drug. The antitumor effects of PS 80-coated PBCA NPs encapsulating GEM were demonstrated using a rat brain tumor model. The 1% PS 80-coated GEM-loaded PBCA NPs were able to cross the BBB, leading to necrosis. As a result, the NPs significantly inhibited the growth of C6 glioma cells in the rat brain tumor model. Furthermore, they significantly extended the survival time compared to the saline-treated control groups [134]. [PERSON] et al. evaluated the toxicity of PS 80-coated NPs encapsulating doxorubicin (DOX) in glioblastoma-bearing rat models. PS 80-coated NPs did not show any changes in toxicity compared to free DOX or DOX-encapsulated NPs. The in vivo toxicity results showed similar effects in both healthy and tumor-bearing rats [135]. [PERSON] et al. synthesized PS 80-coated AmB/PLA-b-PEG NPs for transport across the BBB. The BBB efficiency was significantly enhanced when NPs were coated with PS 80. Figure 7: Schematic representation of PS 80-mediated drug delivery to brain. PS 80 enables the adsorption of ApoE onto NPs, and the PS 80-coated NPs interact with lipoprotein receptors on the BBB’s luminal surface, leading to receptor-mediated transcytosis of the PS-coated NPs. The drug concentration in mouse brain was greatly enhanced compared to non-coated NPs [136]. Super paramagnetic iron oxide NPs (SPIONs) coated with polyethylene glycol (PEG), PEI, and PS 80 were synthesized for effective drug delivery to the brain [137]. A detailed illustration of the BBB transport of PS 80-coated SPIONs is presented in Figure 8. An in vivo study using rat models demonstrated that PS 80-coated SPIONs efficiently crossed normal BBB under an external magnetic field (EMF). The energy dispersive spectroscopy (EDS) results showed that SPIONs accumulated in the cortex near the magnet. Transmission electron microscope (TEM) images and elemental identification through EDS showed efficient accumulation of PS 80-coated SPIONs in the brain. This study demonstrates that both PS conjugation and EMF play crucial roles in the effective passage of SPIONs across the intact BBB [137].
## 6 Experimental Results and Discussion
The high precision obtained with the OzFish-trained model is directly correlated to the lower recall, meaning that the lower quantity of detected objects were the targets, while the CCMAR-trained model presented higher false positives. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & **Dataset** & **Size** & **mAP\({}_{50}\)** & **mAP\({}_{75}\)** & **IoU 1** & **Precision 1** & **Recall 1** \\ \hline YOLOv4 & OzFish & 608 & 48.60\% & 27.56\% & 76.22\% & 94\% 170\% & 35\% 14\% \\ YOLOv4 & CCMAR & 608 & 74.05\% & 43.40\% & 72.29\% & 90\% 185\% & 49\% 166\% \\ \hline YOLOv4-tiny-3l & CCMAR & 608 & 68.62\% & 38.30\% & 78.83\% & 98\% 179\% & 39\% 161\% \\ YOLOv4-tiny & CCMAR & 416 & 61.83\% & 28.38\% & 72.63\% & 92\% 173\% & 38\% 164\% \\ \hline \hline \end{tabular}  
* For IoU threshold at 50%. Confidence threshold at 50% on the left, and 10% on the right.  
\end{table} 
Table 2: Results obtained from testing the models with the marine biologist-annotated images from different locations. It is possible to observe in Table 2, the direct relation between precision and recall with the variation of the confidence threshold. The smaller network models evaluation on unseen images presented exceptional results, with the three YOLO-layered heads obtaining a mAP\({}_{50}\) of 68.62%, and a mAP\({}_{75}\) of 38.30%. The IoU increased in comparison with the previous validation, as presented in Table 1, to 78.83%, including the precision at 98%, although the recall majorly decreased to 39% for a confidence threshold of 50%. After evaluating the reason for such a low recall, two occurrences were detected: the smaller fish being undetected, which can be a result of the use of the YOLO architecture, as was previously mentioned, and a repetitive failure of detecting fishes surrounding the feeder, which also occurred with the training images, as can be observed on the bottom row of Figure 6. The smaller model also reproduced desired outcomes, with the mAP\({}_{50}\) of 61.83%, and with the mAP\({}_{75}\) of 28.38%, the IoU of 72.63%, the precision at 92%, and the recall at 38%, for a confidence threshold of 50%. Nevertheless, these results, specifically with high precision, can be deployed for highly accurate continual underwater station monitoring.
## 2 Materials and Methods
### Part I: Comparison of PosEst and dGNSS Method  
The dGNSS and the PosEst systems were compared for trajectory, speed and acceleration, with the top head point as the reference, as this was the position of the dGNSS antenna. The instantaneous position of the dGNSS antenna was outputted from the geodetic dGNSS post-processing, and for the video method the top head position was annotated through the PosEst system. The local coordinate system from the dGNSS measurement was applied for both systems with the origin at the edge of the in-run, with \(x\)being the longitudinal axis and \(y\) the vertical axis. The axis cross-track was neglected in the description of the results. For comparison of the methods, both the average data of all 16 jumps and paired differences are presented.  
For statistical analysis, t-tests for the time series of the paired difference of each jump were performed using statistical parametric mapping (SPM) with the open-source software package SPM-1D (1-dimensional SPM, www.spm1d.org (accessed on 8 March 2021)); @[PERSON]. [PERSON]). In this method, a t-test is computed for every time point and Random Field Theory [49] is used to compute a threshold test-value (based on the significance level and smoothness of data) and an overall \(p\)-value for supra-threshold clusters, instead of calculating a \(p\)-value for every point. SPM is a relatively new statistical technique in biomechanics, which allows presentation of the time series and statistics graphically, easing the interpretation of the data [50]. The statistical analyses were performed in Matlab R2019b and the level of significance set to \(\alpha=0.05\). In addition, mean absolute error (MAE) between the paired difference of the jumps and zero difference between the methods (i.e., the assumption of the data being identical as they are from the same source) were calculated for each stretch of 5 m along the trajectory to assess how the error between the methods developed throughout the area under analysis.
# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 3 Materials and Methods
### Data Collection  
In order to apply the analyses of the study, the first step, data collection, was carried out. The data were collected by downloading from trendyol.com, an e-commerce website based in Turkey. Trendyol.com is an e-commerce site that serves clothing, accessories, electronics, home and life, supermarkets, cosmetics, and shoes and bags and is frequently preferred by users in Turkey. Trendyol.com, which also has a mobile application, allows customers to shop whenever and wherever they want and to generate product reviews.  
Figure 1: Turkish model process flow.  
In order to create the dataset, the top 50 global cosme brands published in 2023 by the Brand Finance consultancy firm in the UK were targeted. Among these brands, product groups sold on trendyol.com with at least 400 product reviews were selected. In addition, since the study focuses on women's cosmetic product groups, male care product brands in the list were not included in the study. Thus, 981,456 product reviews from product groups belonging to 24 different brands were obtained by data scraping and saved in the csv format. In this study, where the text mining method will be used, only the comments containing emojis or emoticons were deleted and cleaned. Finally, a dataset consisting of 875,445 product comments was prepared. Details on the dataset are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Group** & **Brand** & **Reviews** & **Total Reviews** \\ \hline \multirow{8}{*}{Skin Make-Up Products} & Estée Lauder & 2.985 & \multirow{8}{*}{54.579} \\  & Garnier & 1.123 & \\  & M.A.C. & 7.045 & \\  & L’Oreal & 27.225 & \\  & Maybelline & 16.201 & \\  & Estée Lauder & 823 & \\  & Lancôme & 4.893 & \\ Eye Make-Up Products & L’Oreal & 38.577 & 78.345 \\  & M.A.C. & 4.131 & \\  & Maybelline & 29.921 & \\  & Clinique & 7.750 & \\  & L’Oreal & 2.080 & \\  & M.A.C. & 27.279 & \\  & Maybelline & 25.619 & \\  & Clinique & 17.016 & \\  & Estée Lauder & 10.036 & \\  & Garnier & 48.734 & \\  & La Roche-Posay & 97.661 & \\  & Lancôme & 2.723 & \\ Skin Care Products & L’Oreal & 37.268 & 313.080 \\  & Kiehl’s & 11.742 & \\  & Neutrogena & 3.241 & \\  & Nivea & 47.491 & \\  & Vichy & 2.890 & \\  & Yves Rocher & 34.278 & \\  & Clear & 9.272 & \\  & Dove & 6.293 & \\  & Elseve & 66.461 & \\  & Head\&Shoulders & 6.715 & \\ Hair Care Products & Herbal Essences & 2.103 & 158.505 \\  & L’Oreal & 5.094 & \\  & Pantene & 19.190 & \\  & Vichy & 18.800 & \\  & Yves Rocher & 24.577 & \\  & AXE & 1.359 & \\  & Dove & 15.108 & \\  & Garnier & 13.825 & \\ Body Care Products & Neutrogena & 11.927 & 181.256 \\  & Nivea & 55.162 & \\  & Old Spice & 829 & \\  & Palmolive & 15.364 & \\  & Lancôme & 6.624 & \\ Perfume & Oriflame & 16.733 & 26.699 \\  & Yves Rocher & 3.342 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset.
## 1 Introduction  
Capsule endoscopy (CE) has revolutionized the management of small bowel diseases [1]. Since its introduction into clinical practice, it evolved to include the possibility of a colon assessment as part of a panenteric approach [2]. In fact, due to the opportunistic capture of esophagus and stomach mucosa while performing CE, there has been a growing interest in developing a more comprehensive panendoscopic solution that enables the additional evaluation of these locations [3]. Advances in imaging technologies, such as those enhancing mucosal visualization, further highlight the potential of this method for assessing the gastrointestinal (GI) tract [4].  
Although minimally invasive capsule panendoscopy (CPE) may be tempting and likely well-tolerated by most patients, it presents some technical challenges, including a higher burden for exam interpretation and a greater risk of missing crucial frames [3]. Consequently, the introduction of automated reading assistance methodologies may be crucial for decreasing its reading time while attaining a highly satisfactory diagnostic performance [5]. By doing so, it may be possible to reduce its financial costs, further reinforcing CPE as a cost-effective option [5].  
There are particular challenges with the esophageal assessment by CE. Insufflation and controlled movement are not possible in this procedure [6]. Moreover, the rapid esophageal transit, particularly in the upright position, accounts for the reduced number of esophageal mucosa frames provided by each exam, particularly the Z-line, where frequent pathologies are discovered [7]. While esophagagostroudenoscopy (EGD) remains the gold standard for esophageal assessment, its invasiveness can cause patient discomfort and poses a non-negligible risk of complications [8]. Additionally, certain protocols have been identified to enhance the diagnostic performance of CE in detecting esophageal lesions, by swallowing the capsule in a reclined position, suggesting it may be feasible to detect common esophageal lesions at a fairly comparable level to EGD [9; 10].  
Artificial intelligence (AI) has been a hot topic in the medical community, specifically in areas with a strong imaging component, such as gastroenterology [11]. In the CE technological scenario, machine-learning models are being supplanted by deep-learning (DL) algorithms due to their unsupervised learning capacity [12]. The majority of DL models developed so far are based on convolutional neural networks (CNNs), although there is a growing interest in using vision transformer (ViT) methods to leverage computer vision tasks [13]. While DL models already have demonstrated potential in the automatic detection of lesions in the small bowel and colon first, and, more recently, in the stomach, there is no evidence regarding its use in the esophagus [6; 14; 15].  
The aim of this study was to develop and validate the first deep-learning model capable of the automatic detection of pleomorphic esophageal lesions.
# Prognostic Value of Lymph Node Density in Lingual Squamous Cell Carcinoma
## 3 Results  
### Descriptive Analysis of the Total Population and Population Subgroups According to the LND  
The demographic, clinical, histologic and follow-up analysis of the total study population (n = 62) is shown in the Tables 1-4, as well as the rates with respect to the total sample and the results of the analysis by subgroups according to the LND (\(<\)0.04 and \(\geq\)0.04). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Categories** & **Total Population (n = 62)** & **LND \textless{}0.04** & **LND \textgreater{} 0.04** \\ \hline \multicolumn{3}{c}{* Age (years)} & \multicolumn{2}{c}{60.86 \(\pm\) 14.90} & 62.14 \(\pm\) 14.82 & 57.57 \(\pm\) 14.75 \\ \hline \multirow{3}{*}{Age (years)} & \(<\)40 & 4 (6.5\%) & 3 (75\%) & 1 (25\%) \\  & 40–60 & 25 (40.3\%) & 19 (76\%) & 6 (24\%) \\  & \(>\)60 & 33 (53.2\%) & 27 (81.8\%) & 6 (18.2\%) \\ \hline \multirow{3}{*}{Gender} & Male & 38 (61.29\%) & 30 (78.9\%) & 8 (21.1\%) \\  & Female & 24 (38.7\%) & 19 (79.2\%) & 5 (20.8\%) \\ \hline \multirow{3}{*}{Smoker} & No & 28 (45.2\%) & 22 (78.6\%) & 6 (21.4\%) \\  & Yes & 34 (54.8\%) & 27 (79.4\%) & 7 (20.6\%) \\ \hline \multirow{3}{*}{Drinker} & No & 43 (69.4\%) & 33 (76.7\%) & 10 (23.3\%) \\  & Yes & 19 (30.6\%) & 16 (84.2\%) & 3 (15.8\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of demographic characteristics in the total population and according to LND. Figure 2: Receiver operating characteristic curve analysis of lymph node density ratio cut-off values for predicting overall survival in squamous cell carcinoma of the oral cavity. In the statistical analysis, significant differences were observed in the distribution between the subgroups according to the LND of the categories of the following variables:  
* Neck dissection (\(p\)-value 0.021);
* Pathologic T (\(p\)-value 0.006);
* Pathologic N (\(p\)-value \(<\) 0.001);
* Perineural invasion (\(p\)-value 0.005);
* Pathologic stage (\(p\)-value \(<\) 0.001).
## 4 Comparisons of Test Results and Code Predictions
### Shear Capacity  
Table 6 shows the comparisons of the shear capacity (\(V_{n}\)) of the beams in the S-series specimens with nominal predictions obtained using code equations. The ACI 318-14 equation for the shear transfer capacity of concrete was formulated from the regression analysis of extensive shear data compiled from the slender beam specimens without shear reinforcement. The ACI 440.1R-15 equation considers the reduced shear transfer capacity of concrete in the beams with longitudinal GFRP bars to account for the effect of the decreased compression zone owing to the low elastic modulus of GFRP bars on the shear resistances by compressive concrete and aggregate interlock across the diagonal cracks. The ACI 440.1R-15 equation introduces the ratio of the neutral axis depth to the effective section depth into the shear transfer capacity equation specified in the ACI 318-14 provision. Thus, the shear transfer capacity calculated by the ACI 440.1R-15 equation is lower than that of ACI 318-14 equation, indicating that the reduced values increase with the decrease in the amount of longitudinal GFRP bars. The beam elements of the specimens S-H-C and S-H-G exhibited higher \(V_{n}\) than the predictions calculated by code equations. The ratios between experiments and predictions were similar in both specimens. Although further comparisons have to be conducted, the present beam elements of the exterior beam-column connections made using high-strength materials possessed conservative shear transfer capacities when compared with the code equations.
# Querying on Federated Sensor Networks
## 4 Results
### The Comparison of the Layered and Flat FSN Models
#### 4.2.4 Hypothesis Testing (\(t\)-test)  
We did hypothesis testing (\(t\)-test) to reveal significant improvements caused by the layered FSN management against the flat management. We previously observed that all queries are resolved by both Directed Diffusion and L-FSN with a 100% success rate as shown in Figure 4. Therefore, we compare the flat management by Directed Diffusion and the layered management by L-FSN using their energy consumption (total consumed energy by gateways and nodes) and latency results as given in Sections 4.2.2 and 4.2.3. This test evaluates the hypothesis stating that the mean values of a variable (energy consumption or latency) observed over time are equal for both of the protocols. We used 0.001 level of significance for \(t\)-tests. We found that the means of energy consumption and latency results of these two protocols are significantly different at \(p<0.001\). Thus, \(t\)-test results prove the observation of performance improvement using the layered FSN management by L-FSN compared to flat management.
# Demosaicing by Differentiable Deep Restoration
## 3 The Proposed Method  
### Overview  
We design a deep restoration method for demosaicing that estimates the color image \(I\) by minimizing  
\[||M-HI||^{2}. \tag{1}\]  
This problem is under-constrained even \(H\) is known, due to the sub-sampling nature of the degradation model.  
The pipeline of the proposed method is illustrated in Figure 1. The input raw image goes through a Pattern Sharing Convolution (PSC) layer as a preprocessing. This preprocessing is necessary, because the data in the raw image is sparse where each pixel has only the intensity of one color band. This is not appropriate for CNNs to directly operate on. After that, we use a U-Net to extract image features and generate some bases \(B_{1},B_{2},\cdots B_{m}\). We consider the result \(I\) to be a linear combination of these bases within local image patches, i.e., \(I^{(i)}=v_{1}^{(i)}B_{1}^{(i)}+v_{2}^{(i)}B_{2}^{(i)}\cdots+v_{m}^{(i)}B_{m}^ {(i)}\). Here, the superscript \({}^{(i)}\) indicates the \(i\)-th \(n\times n\) local image patch (\(n=5\) in our experiments). Here, both the full-color image patch \(I^{(i)}\) and the bases patches \(B_{j}^{(i)}\) are 3-channel image patches. We solve the combination coefficients \(V^{(i)}=[v_{1}^{(i)},v_{2}^{(i)},\cdots v_{m}^{(i)}]^{\top}\) by minimizing Equation (1) in closed-form solution. Note that the coefficient \(v_{1}^{(i)},v_{2}^{(i)},\cdots v_{m}^{(i)}\) are scalars, which means they are shared by the red, green, and blue channels. We choose this design for strong correlation between different color channels.  
As illustrated in Figure 2, we slide these patches over the image and solve the result at the patch center. In our implementation, the processing of different patches is parallelized by GPU processing.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation
### Problem Formulation and Mathematical Modeling
#### 3.2.4 Energy Efficiency  
The number of bits transmitted per joule of energy (b/j), called \(EE\)[44], is  
\[EE=\frac{C}{P_{tot}^{BS}}, \tag{18}\]  
where \(P_{tot}^{BS}\) is the total power consumed and can be calculated by the following equation [45]:  
\[P_{tot}^{BS}=N_{TRX}\Bigg{(}\frac{\frac{P_{tx}}{\eta_{PA}}+P_{RF}+P_{BB}}{(1- \sigma_{DC})(1-\sigma_{MS})(1-\sigma_{Col})}\Bigg{)}, \tag{19}\]  
where \(\sigma_{DC}\), \(\sigma_{MS}\) and \(\sigma_{Col}\) denote losses incurred by the DC-DC power supply, main supply, and cooling, respectively; \(P_{tx}\), \(P_{RF}\) and \(P_{BB}\) are the output power per transmit antenna, radio frequency, and baseband power, respectively; \(\eta_{PA}\) denotes the power amplifier (PA) power efficiency; and \(N_{TRX}\) is the number of transceiver, which can be computed as follows [45]:  
\[N_{TRX}=N_{Carr}\times N_{Sect}\times N_{Ant}, \tag{20}\]  
where \(N_{Carr}\), \(N_{Sect}\), and \(N_{Ant}\) denote the number of carriers, sectors, and antennas, respectively.