esa-sceva/satcom-chunk-collection huggingface dataset:
# IPAQ-L and CPET Usefulness in a North-Eastern Romanian Population Undergoing Cardiac Rehabilitation
## 4 Discussion
While several authors have previously reported a natural inverse relation between physical activity and excess weight [22; 29], other studies have found an uncommon positive association between total PA and BMI (especially among females), which could be explained by health consciousness or apparent motivation [30; 31; 32; 33]. [PERSON] et al. [33] reported a strong negative association between middle age (40-49 years) and total PA in their study group, but also failed to show a substantial association between BMI and total PA. Although a recent study [33] showed that male gender and the 30-39-year-old age group are associated with a higher total sitting time,we found no significant differences regarding sitting time between males and females or within the three analyzed age groups. CPET is a clinical method that allows for a global assessment of cardiorespiratory function in order to determine exercise capacity. It allows objective measurement of both submaximal and peak exercise responses using measures of respiratory oxygen uptake, carbon dioxide output and ventilatory measures. Previous reports highlight that the CPET is often used prior to major surgery to assess functional potential in patients and aid clinical decision-making and risk assessment [34]. Moreover, a recent study assessing the utility of CPET in patients recovering from COVID-19 suggested it as a potentially useful method for detecting ventilatory and cardiovascular changes in COVID-19 [35]. Therefore, for subjects admitted to a cardiovascular and pulmonary rehabilitation program, CPET can be useful as a screening device for exercise capacity and cardio-ventilatory limitations. According to our results, the association of IPAQ-L with CPET could offer valuable information in specific populational groups. A previous study reported a moderate correlation between IPAQ Short Form results and treadmill stress test performance [36]. Our analysis showed that MTCF as a measure of fitness level exhibited a significant positive correlation with total MET-minutes/week at work and a borderline correlation with total physical activity MET-minutes/week. However, a study of a larger population is required to confirm the latter correlation. According to a recent study, IPAQ was not appropriate for assessing PA in cardiovascular disease patients because the subjects often recorded severe PA values, resulting in non-homogeneous outcomes [37]. Although the IPAQ questionnaire can provide a comprehensive image of a patient's PA pattern on an individual basis, we cannot suggest using IPAQ-L in the study of a wider community of subjects with various cardiovascular disease due to the broad variability of the responses reported by our patients (similar to those of [PERSON] et al. [37]). Although other authors have used the IPAQ questionnaire in the analysis of PA among Romanian students [38; 39; 40] and in the geriatric population [41], to our knowledge, this is the first analysis of PA levels using the IPAQ-L and CPET in Romanian patients with cardiovascular comorbidities.
# Computational Complexity of Modified Blowfish Cryptographic Algorithm on Video Data
## 4 Discussion
### Performance Evaluation
#### 4.1.1 Performance Comparison Based on Execution Time  
The encryption and decryption in Blowfish and the modified Blowfish algorithm were conducted for videos of varying sizes and formats. The time required for the encryption and decryption operations is computed for various video sizes (in kilobytes) and the key size also varies accordingly. The overall execution time includes both encryption and decryption time. The execution time for each video and the average execution time has been calculated as shown in Table 2.  
The performance analysis of the Blowfish and modified Blowfish techniques is shown in Table 2 and Figure 6. The results reveal that the modified Blowfish algorithm performs faster than the original Blowfish Algorithm based on the average execution time  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Blowfish Algorithm**} & \multicolumn{3}{c}{**Modified Blowfish**} & \multirow{3}{*}{**Modified Blowfish**} \\
**Video Size** & \multirow{2}{*}{**Key Size**} & \multirow{2}{*}{**Decryption**} & \multirow{2}{*}{**Encryption**} & \multirow{2}{*}{**Decryption**} & \multirow{2}{*}{**Decryption**} & \multirow{2}{*}{**Algorithm**} & **Blowfish** \\
**(Kilobytes)** & & & & & & & \\ \cline{3-7}  & & **Encryption** & **Time (ms)** & **Time (ms)** & **Time (ms)** & & **Algorithm** \\ \hline
187.0 & 12 & 25.8 & 26.1 & 23.9 & 24.5 & 51.9 & 49.5 \\
342.0 & 12 & 2  
of the two methods. (Average execution time is 250.0 ms for Blowfish and 248.4 ms for modified Blowfish.)
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 7 Experimental Test Environment
### VLC Media Player Software Configuration  
Video LAN Client (VLC) Media Player [25] was configured with specific settings to optimize video transmission and playback performance for streaming and receiving video over both the MPT-GRE tunnel and a single path. The following steps outline the adjustments made to facilitate the video streaming process:  
1. _Video Source Selection_: The video file was first added to the stream on the source machine, which acts as the server in this experiment.
2. _Transcoding Configuration_: To ensure compatibility and efficient streaming, the H.264 codec was selected for video transcoding, and the MP3 codec was chosen for audio. These codecs were selected due to their widespread use in video streaming and ability to balance compression and quality.
3. _Destination Setup_: The MPT-GRE tunnel address or the IP address of the first path was specified as the destination for video transmission, along with a designated port number. Using specific port numbers helped segregate traffic between single-path and multipath scenarios.
4. _Protocol Selection_: HTTP protocol was employed for video streaming in single-path and multipath environments. This choice was made because HTTP adaptive streaming is commonly used for reliable video delivery across diverse network conditions.
5. _Video Recording at the Destination_: On the destination machine, VLC's recording feature was utilized to save the video received through either the MPT-GRE tunnel or the first path address. This allowed for the post-transmission analysis of the video quality and performance.
## 3 Results
### Canopy Height
#### 3.2.2 Vegetation Cover  
To assess CASAL's forest canopy height inversion capability across varying vegetation densities, vegetation cover was categorized into five intervals as follows: 0.2-0.4, 0.4-0.6, 0.6-0.8, 0.8-0.9, and \(>\)0.9. Table 7 and Figure 10 present accuracy metrics, including the correlation coefficient (\(R^{2}\)), Bias, and median absolute deviation (MAD), for CASAL-RH90 and ALS-Hmax across different vegetation cover intervals. Table 7 also includes root mean square error (RMSE) values alongside \(R^{2}\), Bias, and MAD for CASAL-RH90 and ALS-Hmax.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Slope** & **Number** & \(\mathbf{R^{2}}\) & **Bias (m)** & **RMSE (m)** & **MAD (m)** \\ \hline \(<\)5\({}^{\circ}\) & 1286 & 0.574 & \(-\)0.67 & 2.67 & 2.07 \\
5-10\({}^{\circ}\) & 3057 & 0.542 & \(-\)0.45 & 2.40 & 1.8 \\
10–15\({}^{\circ}\) & 3017 & 0.533 & \(-\)0.35 & 2.36 & 1.93 \\
15–20\({}^{\circ}\) & 2305 & 0.518 & \(-\)0.33 & 2.57 & 2.15 \\
20–25\({}^{\circ}\) & 1782 & 0.484 & \(-\)0.31 & 2.87 & 2.41 \\ \(>\)25\({}^{\circ}\) & 1993 & 0.466 & \(-\)0.17 & 3.55 & 2.76 \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy of CASAL-derived canopy height estimates stratified by slope class.  
Figure 9: Scatter plots comparing CASAL-RH90 and ALS-Hmax canopy height estimates across six different categories of slope categories: (**a**) 0–5\({}^{\circ}\), (**b**) 5–10\({}^{\circ}\), (**c**) 10–15\({}^{\circ}\), (**d**) 15–20\({}^{\circ}\), (**e**) 20–25\({}^{\circ}\), and (**f**) \(>\)25\({}^{\circ}\). The least-squares regression line (black) and 1:1 reference line (red) are superimposed for comparative analysis.  
Hmax across vegetation cover categories. Analysis reveals that \(R^{2}\) between CASAL-RH90 and ALS-Hmax initially increases before decreasing with higher vegetation cover. The maximum \(R^{2}\) (0.598) occurs at 0.4-0.6 vegetation cover, demonstrating CASAL's optimal performance for forest canopy height estimation within this range. However, \(R^{2}\) drops to 0.397 when the vegetation cover exceeds 0.9, indicating significantly reduced inversion accuracy. Similarly, Bias, RMSE, and MAD values exhibit an initial decrease followed by an increase with higher vegetation cover. At 0.4-0.6 vegetation cover, Bias (\(-0.56\) m), RMSE (\(3.12\) m), and MAD (\(2.76\) m) all reach their minimum values. At vegetation cover \(>\)0.9, Bias increases to \(-0.43\) m, RMSE to \(2.89\) m, and MAD to \(2.28\) m, reflecting substantially lower inversion accuracy.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 5 Integration of IMES Radio and PDR Information
The system matrix \(\mathbf{\mathrm{F}}^{k}\) and observation matrix \(\mathbf{\mathrm{H}}^{k}\) can be linearized according to Jacobians as follows:  
\[\mathbf{X}=[x,y,\theta]^{T} \tag{11}\]4 \[= \begin{pmatrix}1&0&-sin(\theta)\Delta d\\ 0&1&cos(\theta)\Delta d\\ 0&0&1\end{pmatrix} \] \[\mathbf{\mathrm{H}} =\frac{\partial h}{\partial\mathbf{\mathrm{X}}}|_{\mathbf{\mathrm{x}}= \mathbf{\mathrm{x}}}\] \[= \begin{pmatrix}\frac{x-X_{1}}{\sqrt{(x-X_{1})^{2}+(y-Y_{1})^{2}} }&\frac{y-Y_{1}}{\sqrt{(x-X_{1})^{2}+(y-Y_{1})^{2}}}&0\\ \frac{x-X_{2}}{\sqrt{(x-X_{2})^{2}+(y-Y_{2})^{2}}}&\frac{y-Y_{2}}{\sqrt{(x-X_{ 2})^{2}+(y-Y_{2})^{2}}}&0\\ \vdots&\vdots&\vdots\\ \frac{x-X_{N}}{\sqrt{(x-X_{N})^{2}+(y-Y_{N})^{2}}}&\frac{y-Y_{N}}{\sqrt{(x-X_{ N})^{2}+(y-Y_{N})^{2}}}&0\end{pmatrix}  \tag{17}\]  
\(\mathbf{\mathrm{X}}^{k}\) denotes the state variable at time step \(k\); \(\mathbf{\mathrm{X}}^{k+1|k}\) the estimate of the state variable at time step \(k+1\) given the current state \(\mathbf{\mathrm{X}}^{k}\); \(\mathbf{\mathrm{X}}^{k+1|k+1}\) the final estimate of the state variable at time step \(k+1\). These estimates are assumed to have a Gaussian probability density function, with their covariance matrices defined as \(\mathbf{\mathrm{P}}^{k}\), \(\mathbf{\mathrm{P}}^{k+1|k}\), \(\mathbf{\mathrm{P}}^{k+1|k+1}\), respectively. Let \(\mathbf{\mathrm{Z}}\) and \(\mathbf{\mathrm{Z}}^{k+1}\) denote the observation variable at time step \(k\) and \(k+1\), respectively; thus, the EKF will generate the estimate \(\mathbf{\mathrm{X}}^{k+1|k+1}\) at time \(k+1\) given its previously estimated \(\mathbf{\mathrm{X}}^{k|k}\) at time \(k\) and current observation \(\mathbf{\mathrm{Z}}^{k+1}\). The processing of the EKF can be divided into the prediction process and the update process. During the prediction process, the estimated \(\mathbf{X}^{k+1|k}\) and its covariance matrix \(\mathbf{P}^{k+1|k}\) will be predicted according to the following formula:  
\[\mathbf{X}^{k+1|k} =f(\mathbf{X}^{k},\mathbf{U}^{k},\mathbf{\omega})\] \[\mathbf{P}^{k+1|k} =\mathbf{P}^{k}\mathbf{P}^{k|k}\mathbf{P}^{k}+\mathbf{\Omega} \tag{18}\]  
During the update process, the final estimate \(\mathbf{X}^{k+1|k+1}\) and its covariance matrix \(\mathbf{P}^{k+1|k+1}\) are calculated according to the predicted estimate \(\mathbf{X}^{k+1|k},\mathbf{P}^{k+1|k}\) and the current observation \(\mathbf{Z}^{k+1}\) according to the following formula:  
\[\mathbf{X}^{k+1|k+1} =\mathbf{X}^{k+1|k}+\mathbf{R}(\mathbf{Z}^{k+1}-h(\mathbf{X}^{k+ 1|k},\psi))\] \[\mathbf{P}^{k+1|k+1} =\mathbf{P}^{k+1|k}-\mathbf{R}\mathbf{H}^{k+1}\mathbf{P}^{k+1|k} \tag{19}\]  
where \(R\) and \(S\) are defined as follows:  
\[\mathbf{S} =\mathbf{H}^{k+1}\mathbf{P}^{k+1|k}\mathbf{H}^{k+1T}+\mathbf{\
abla}\] \[\mathbf{R} =\mathbf{P}^{k+1|k}\mathbf{H}^{k+1T}\mathbf{S}^{-1} \tag{20}\]  
According to the prediction and update processing of the EKF, the state variables can be tracked continuously in each time step.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### kNN Classification Algorithm  
In order to assign a sentiment label for each tweet in \(TT\), we apply a \(k\)NN strategy. Initially, we build the feature vectors for all tweets inside the training and test datasets (\(F_{T}\) and \(F_{TT}\), respectively). Then, for each feature vector \(u\) in \(F_{TT}\), we find all the feature vectors in \(V\subseteq F_{T}\) that share at least one word/n-gram/pattern feature with \(u\) (matching vectors). After that, we calculate the Euclidean distance \(d(u,v),\forall v\in V\) and keep the \(k\) lowest values, thus forming \(V_{k}\subseteq V\) and each \(v_{i}\in V_{k}\) has an assigned sentiment label \(L_{i},1\leq i\leq k\). Finally, we assign \(u\) the label of the majority of vectors in \(V_{k}\)If no matching vectors exist for \(u\), we assign a \"neutral\" label. We build \(C\) by adjusting an already implemented A&NN classifier in MapReduce to meet the needs of opinion mining problems.
## 3 Mesh Size Calibration  
Figure 5a shows how a cube with \(L_{C}\) = 0.45 m edge length was placed inside a cylinder of 6 m length and 2 m diameter to determine mesh size. By considering the input boundary condition as a constant velocity, the total resistance of the cube against fluid flow is estimated. A cube was analysed against a constant 10 m/s fluid flow to examine the mesh size dependency. The numerical results showed that uniform mesh sizes (_ms_) with \(ms/L_{C}\) less than 0.09 did not significantly affect the total resistance as shown in Figure 5b.  
Accordingly, the developed model was used to estimate the resistance coefficient (\(C_{R}\)) of the cube by using Equation (15) against fluid flows with different velocities (\(V\)), and the results are shown in Figure 6a. Based on the results shown in Figure 6a, the average resistance coefficient for different speeds is calculated to be about 1.13. According to the literature [34; 35; 36], the resistance coefficient of a cube is estimated to be between 1.05 and 1.20. Therefore, the average of 1.13 was found to be in very good agreement with the amount suggested by other researchers.  
\[C_{R}=\frac{2R}{\rho V_{f}^{2}L^{2}} \tag{15}\]  
Figure 5: (a) The generated mesh of the cube inside a cylinder in OpenFOAM, (b) Mesh size dependence evaluation.  
The cube exhibits significantly more vortex shedding than the submarine's bare hull, so another mesh size verification is performed using the sphere. Thus, a sphere with a diameter of 2.64 m is also computed with the same mesh size. Figure 6b shows the resistance force and coefficient for fluid flows with different Reynolds numbers, where an average resistance coefficient of 0.45 was calculated. The resistance coefficient of a sphere is estimated in the range of 0.4 and 0.5 for Reynolds numbers greater than \(10^{4}\)[36]. Consequently, the average of 0.45 was found to be very close to the amount suggested in [37].
## 1 Introduction
Considering the high reflectivity of vegetation in the NIR region, it is unproblematic to detect the mentioned features in remote sensing images. On the other hand, extraction of urban objects such as roads and buildings is more challenging, since they have more similar spectral reflectance and texture. However, buildings have more compact geometric shapes, while roads are typically elongated features. Hence, shape parameters can be of great help in the delineating of buildings from roads. Contextual information is also a good tool for VHRSI classifications; for example, buildings are elevated objects so there are shadows associated with them in the direction opposite to sun's azimuth. Therefore, if an urban object has a shadow in the related direction, it is a building [9]. Here, the role of object-based image analysis is underscored, as the shape and neighborhood of the objects can be defined in object-based image analysis, not in pixel-based methods. In this article, first data specifications are presented. Then, the fuzzy-based methodology is explained and fuzzy membership functions and fuzzy rules are introduced. Finally, the accuracy assessment is performed and the results are compared to the same classification method but with crisp thresholds. In this paper the offered method is tested on two different data sets (GeoEye and QuickBird imagery) and the results are compared to the results of test projects with crisp thresholds.
## 1 Introduction  
The amount of antibiotics released into the environment is increasing owing to the lack of appropriate disposal methods and strict control measures, which threaten human health and the ecosystem [1, 2]. Oxytetracycline (OTC), an antibiotic, is widely used as an antimicrobial agent and growth factor in drug therapy and the livestock industry [3, 4]. Approximately 70% of OTC leaves organisms via urine and feces without undergoing metabolism because of its poor absorption [5]. Therefore, OTC has been detected in various environments, such as aquatic systems, soil, and sediments [6, 7, 8]. In aquatic environments, several studies have reported that OTC has been detected in river water [9] and in the influent and effluent of a wastewater treatment plant [10, 11, 12, 13]. However, it is difficult to degrade the released OTC in water using conventional wastewater treatment processes [14]. Therefore, effective treatment of OTC in water is a problem that needs to be solved urgently.  
Sulfate radicals (SO\({}_{4}\bullet^{-}\)) and hydroxyl radicals (HO\(\bullet\)) are widely used reactive radical species in wastewater treatment because of their high oxidizing capabilities [15]. Generally, activating peroxides such as persulfate (PS), peroxymonosulfate, and hydrogen peroxide or photocatalytic processes can generate these radical species [16]. Among the above-mentioned peroxides, PS is much cheaper and easier to activate owing to its low band energy (140 kJ/mol) [17, 18], PS has attracted attention as an oxidant for degrading various pollutants [19; 20]. Catalysts such as metal-containing oxides and transition metals have been used to activate PS because they are energy-free and economic [21; 22; 23]. Moreover, research on the reuse of waste containing metal elements such as Fe as a PS activator has been conducted [16; 24; 25].  
Disposable hand warmers are widely used to keep oneself warm; thus, the demand for hand warmers greatly increases in winter. After exposure to air, the materials in the hand warmer pocket react and release heat for a period of time. The spent hand warmer is then discarded, which can adversely affect the environment and lead to wastage of resources [26], recycling or reusing the spent hand warmer is needed to reduce environmental pollution. Hand-warmer waste generally contains iron oxide (Fe\({}_{2}\)O\({}_{3}\)) particles. Therefore, reusing hand-warmer waste for the activation of PS can be an environmentally friendly and cost-saving technique. To the best of our knowledge, this study is the first to recycle hand-warmer waste as a catalyst for PS activation.  
In this study, a hand-warmer waste catalyst (HWWC) was prepared by a simple magnetic separation method and used as a PS activator for OTC degradation. The surface morphology and crystal structure of the prepared HWWC were investigated. The effects of the catalyst dosage, PS concentration, and pH on the degradation of OTC were studied. In addition, the stability of the catalyst was evaluated by conducting a reuse test.
# Flexible Phased Antenna Arrays: A Review
## 1 Introduction
### Silicon-Based RFIC Technology and Diverse Applications  
The advancement of silicon-based RFIC technology led to fully integrated PA systems operating across multiple frequency bands since the late 2000s. Notable examples include Ka-band TX/RX on-chip systems [14], Q-band PA TX based on RFIC [15], and system-level implementations in X/Ku-band using single silicon chips [16], demonstrating complex PA architectures for various applications [17]. This evolution enabled compact and power-efficient solutions for 5G devices [18], where machine learning techniques have also been explored for PA synthesis [19]. PA have also been incorporated in Wireless Power Transfer (WPT) systems, such as near-field neuro-modulation [20] and channel-based intelligent WPT with phase control for beam steering [21]. Further applications include medical imaging and meteorological remote sensing [22; 23]. Beam steering and amplitude tapering previously required element-wise calibration, but this was addressed in 2017 by a fully functional RFIC-based PA with orthogonal phase and amplitude control for 5G communications [24]. These developments were later adopted in SATCOM applications [25; 26]. Silicon-based RFICs enabled efficient and compact PA systems with wide-band and wide-scan performance, scalability, high Effective Isotropic Radiated Power (EIRP), and improved Cross-Polarization Discrimination (XPD) [27]. The use of RFIC beamformer chips with Wilkinson combiner networks on Printed Circuit Board (PCB) allowed polarization-agile PA systems with high polarization purity and simultaneous multi-beam control [28; 29; 30]. Integration of multiple functions on a single chip reduced discrete components, improving form factor and lowering cost [31]. In 2014, a wafer-scale approach combining all components on the same silicon wafer further enhanced array compactness [32]. By addressing size, cost, efficiency, and scalability, silicon-based RFIC technology has enabled the transition from bulky, mechanically steered systems to compact AESAs, opening opportunities in consumer electronics, automotive, and wearable technologies.
## 4 Discussion
We can understand the basis for the image classification determination and the reason for accurate correction. In addition, the error in the predicted parking lot images in the RSSCN7 image dataset was reviewed via XRAI analysis. As shown in Figure 15, the hot area in the original image did not capture the car grid of the parking lot, and only focused on the side eaves and trees along the street, which were strengthened after correction. The light and shadow of the edge of the car grid makes the parking lot grid more evident. Comparing with Figure 15, it can be seen that the characteristic hot zone of XRAI contains a large number of parking lot grids from Figure 15, and finally, the correct classification category is obtained in the revised prediction. Figure 13: Image (river/lakes category) with XRAI analysis. (**a**) Original image (predicted as Industry); (**b**) improved image. In this study, XRAI is used to visualize the heatmap based on the model prediction. We try to understand where the feature texture in the image has an effective basis for decision-making. Additionally, we evaluate the image to understand the feature texture of the image block and verify the correlation of the model prediction category. When considering the transfer of the salient feature position and the basis for the analysis and determination of  
Figure 14: Image (river/lakes category) with XRAI analysis. (**a**) Original image (predicted as field); (**b**) improved image. Figure 15: Parking lot image with XRAI analysis. (**a**) Original image (predicted as industry); (**b**) improved image. the auxiliary verification using the image correction method, we can improve the model prediction under the condition of intelligibility and obtain better prediction accuracy. Tables 9 and 10 show the comparison of the overall performance on RSSCN7 and WHU-RS19 datasets. The performance of the proposed method in this study is better than the existing state-of-the-art approaches. This is because these existing methods exclusively applied original images for model training. However, this study adopts an image quality enhancement strategy in the deep learning model training. This image preprocessing method can enhance the input images' quality, train the deep learning model to extract more effective image features, and improve the deep learning model's overall performance.