esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Parametric Investigation of UV-A/TiO\({}_{2}\) Process
#### 3.2.3 The Effect of Catalyst Loading  
In this study, the effect of TiO\({}_{2}\) load was also investigated by testing three different concentrations, 50 mg/L, 100 mg/L, and 300 mg/L. In all of the experiments, SDX was completely degraded, but slight variations were observed regarding the mineralization. Specifically, the experiments with 50 mg/L showed 60 \(\pm\) 2% of mineralization, the ones with 100 mg/L of catalyst showed 77 \(\pm\) 2% TOC removal, while in the experiments with 300 mg/L, the performance dropped again at 65 \(\pm\) 4%. These findings are in accordance with the literature, as previous studies on heterogeneous photocatalysis indicate that an increase in catalyst concentration leads to a proportional increase in active surface area, while excessive loads of suspended particles can hinder the penetration of photons into the reactor fluid and thus impede the photocatalytic reactions [31; 32; 50]. The concentration of 100 mg/L was found to be optimal and was used for the next experiments.
## 4 Discussion  
Towards developing an efficient method for optimal attenuation of multi-source noise/interference in biopotential signal acquisition, this study proposed hardware-based shielded drive technology. Importantly, the proposed method could efficiently shield the recorded biopotential signals from extraneous interferences as much as possible and also preserve the signal quality during the transmission process to obtain a high quality target signal prior to entering the acquisition device. In this regard, three different types of shielded drive methods were developed and their performances were evaluated. The evaluation was done with respect to the capability of methods to attenuate the effect of two kinds of extraneous interferences using a four-electrode channel recording system across three kinds of biopotential signals (ECG, EOG and EMG).
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 6 ML Applications in Energy Prediction  
Applying ML to energy efficiency and management in smart buildings has completely changed the game. It becomes clear when we explore ML applications in energy prediction that these technologies are essential to attaining operational excellence, sustainability, and energy efficiency rather than merely being auxiliary tools. The transition from conventional reactive models to proactive, data-driven tactics is highlighted in this portion of the article as it examines the various uses of ML in intelligent infrastructure energy consumption prediction and management. A crucial part of BEMS is energy prediction, which entails projecting future energy requirements to maximize consumption, cut expenses, and preserve grid stability. A paradigm shift has occurred in this field with the introduction of ML, allowing for the development of more dynamic, flexible, and accurate prediction models that can adjust to the always-shifting demands for energy. These developments have made it easier to anticipate short-term and long-term energy use, predict and monitor loads, perform NILM, and optimize energy use in real time. ML is used by short- and long-term forecasting models to anticipate energy needs over various periods, facilitating effective resource allocation and minimizing waste. In contrast, load prediction and monitoring aim to improve demand-side management by comprehending and forecasting a building's energy usage trends. With the aid of ML, NILM is a revolutionary approach that provides granular insights into energy utilization by breaking down overall energy consumption data into appliance-level usage without the need for individual monitors. The ultimate use of ML in energy management is real-time optimization, which dynamically modifies energy use in response to real-time data to guarantee optimal operation of the energy systems. Applying ML to energy prediction and management is a major step toward intelligent, sustainable, and user-centered building environments [98]. It goes beyond simple technological improvement. We will delve into the workings of ML models, the difficulties associated with implementing them, and the genuine advantages that they offer in the context of energy management and smart buildings as we examine these applications in the parts that follow.
## 3 Method
### Data Processing  
Since the absolute radiation correction data of the Luojia-1 nighttime light data are floating-point data, which are not convenient for storage, the ground system enlarged the floating-point radiance data to the 10 th power of 10, and the exponential stretch was converted to INT32 storage [37]. Before building the LJ&POI index, it was necessary to perform radiant brightness conversion operations according to the given formula. The formula for calculating the radiant brightness of the Luojia-1 nighttime light data product is, according to the Data and Application Network of the Hubei Province High-Resolution Earth Observation System, as follows:  
\[L_{i}=DN_{i}^{3/2}\cdot 10^{-10} \tag{1}\]  
where \(Li\) is the radiant value after absolute radiation correction at point \(i\) (W/(m\({}^{2}\cdot\)sr\(\cdot\)um)), and \(DNi\) is the image grayscale value at point \(i\). The results before and after data correction are shown in Figure 3.  
Figure 2: Workflow diagram of the estimation of the HVR.  
The POI data can accurately reflect information such as business activities and infrastructure in the region. Before constructing the LJ&POI index, the POI data need to be cleaned and analyzed by nuclear density analysis, and the core density distribution map of the POI data in the study area can be obtained.  
Nonparametric kernel density estimation (KDE) is a method that does not require prior assumptions regarding the distribution. The data points that are close to the center point in the calculation area have higher weights and vice versa based on the distance from the center point. Therefore, the results obtained were the weighted average density of all points in the study area. The kernel density (\(P_{i}\)) at any point (\(i\)) was calculated as follows:  
\[P_{i}=\frac{1}{n\pi R^{2}}\times\sum_{j=1}^{n}k_{j}(1-\frac{D_{ij}^{2}}{R^{2}} )^{2} \tag{2}\]  
where \(n\) is the number of data points (\(j\)) in the calculation rule area; \(R\) is the bandwidth of the calculation rule area; \(K_{j}\) is the weight of data point \(j\); \(D_{ij}\) is the Euclidean distance between spatial point \(i\) and data point \(j\). In this paper, it was necessary to reasonably select the bandwidth (\(R\)) according to the scientific problem, and since this paper needed to use the POI kernel density to extract the built-up areas with local detailed information [38], we selected a 130 m bandwidth (same as Luojia-1). The results are shown in Figure 4.  
Figure 3: Comparison of (**a**) before and (**b**) after image correction.
## 3 Results
### Quality of Life Assessment  
The results of SF-36 have shown significant improvement in overall QoL (60.2 vs. 70.7, \(p\) < 0.001) and both major scales (physical and mental components of QoL) after HTR (for PCS: 40.8 vs. 45.5, \(p\) < 0.001; for MCS 55.6 vs. 63.8, \(p\) < 0.001). There were also changes observed in subscales referring to physical functioning (\(p\) < 0.001), role limitations because of physical problems (\(p\) = 0.003), bodily pain (\(p\) < 0.001), vitality (\(p\) < 0.001), and role limitations because of emotional problems (\(p\) = 0.022). All results suggest favorable changes after the HTR. There were no changes in the health perception or social functioning after the HTR in HF patients with LVAD. The details are presented in Table 3 and Table S2. There were no statistically significant correlations between the outcome variables and QoL and the patient's age and BMI, apart from a negative correlation of BMI with \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Before HTR** & **After HTR** & \(p\) \\ \hline \multicolumn{4}{c}{Cardiopulmonary exercise test} \\ \hline Workload duration (s) & 628.4 \(\pm\) 203.8 & 728.1 \(\pm\) 22.2 & 0.014 \({}^{*}\) \\ Peak VO\({}_{2}\) (ml/kg/min) & 12.5 \(\pm\) 2.9 & 15.1 \(\pm\) 3.0 & \textless{}0.001 \\ \% predicted peak VO\({}_{2}\) & 39.0 [34.0 -49.0] & 53.0 [44.0 -59.0] & \textless{}0.001 \\ RER & 1.11 \(\pm\) 0.12 & 1.12 \(\pm\) 0.09 & 0.664 \\ HR rest & 78.7 \(\pm\) 12.3 & 73.8 \(\pm\) 12.8 & 0.060 \\ HR max & 105.6 \(\pm\) 15.6 & 113.2 \(\pm\) 21.3 & 0.065 \\ \hline \multicolumn{4}{l}{HTR: home-based telerehabilitation; HR: Heart Rate; RER: Respiratory Exchange Ratio; peak VO\({}_{2}\): peak oxygen consumption; Data presented are mean values \(\pm\) standard deviation. Statistical significance: \(p\) < 0.05. \({}^{*}\) The appropriate statistical test (parametric or nonparametric) was selected depending on the distribution of differences between the 2 measurements (after minus before) and not on the measurements themselves. Since the distribution of the difference is skewed (for the Shapiro-Wilk test: \(p\) = 0.011), a non-parametric test was used (here and in the original version.} \\ \end{tabular}
\end{table} 
Table 2: Comparison of outcomes before and after home-based telerehabilitation.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Network State Representation
#### 3.1.3 Radio Resource Occupancy (\(C^{k\times m}\)) in the Previous 1 s Time Window  
This parameter indicates the number of resource blocks (RBs) occupied in each subchannel \(k\) and timeslot \(m\) within the selection window \(T_{sel}\)[19; 20].  
The number of resource blocks occupied in subchannel k and timeslot m are given by  
\[C^{k\times m}=\sum_{k\ \in\ K}\sum_{m\ \in\ M}\ RB_{s} \tag{2}\]  
where \(k\) is the set of subchannels, and \(m\) is the set of timeslots.  
We capture the temporal variations of these parameters over the previous time window using a GRU-based recurrent neural network (RNN). These components are captured and processed in the system using gated recurrent units (GRUs). The GRUs track temporal variations in these parameters and use them as multi-dimensional inputs to predict future states, aiding in more informed decision making regarding resource allocation.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 4 Proposed Approach
### Situation Rules Learning Process  
The proposed learning process classifies situation rules according to semantic rule ontology. The learning process takes a collection of situations rules and outputs classes of learned rules. It consists of the following four main steps (Figure 3).  
**Step 1--Collection of rules profiles:** consists of collecting situation's rules from vast platforms and social media then sorting them according to users rating from the most popular until the less popular. After the collection of new situations, the system sends them to the preprocessing module for filtering and prepossessing.  
**Step 2--Processing and filtering rules profiles:** this consists of eliminating redundant and insignificant rules. For instance, rule without consequence and rule without antecedent.  
**Step 3--Ontology-based rules analysis and classification:** We analyze the situation rules with the defined classes in MULTI-OSCM ontology to decide in which class we classify them. From situation rules, we extract a list of semantic context data relates to user's role, user's location, or both from WordNet [14]. For user's location, WordNet gave semantic data, such as Home, Work, University, Office or Car. For user's role, WordNet gives semantic data, such as Citizen, Worker, Student or Driver. We define three classes that represent generic context rules according to the location and the role of user: role and location-based, location-based and role-based. A given situation rule is classified into one of these generic classes based on the location of a user (Home, University, Office,  
Figure 3: An overview of situation rules learning process.  
Hospital, Car, City, Parking or Market) and his role (Citizen, Student, Worker or Driver). If the condition of rule contains both location and role terms, then we classify it into role and location-based classes. Otherwise, we check if it contains only location terms, so we classify it into location-based classes. If not, we classify it into a role-based class. Then, we determine the most popular situation rules based on the users' ratings. The user's rating is ranging from 1 (useless rule) to 5 (excellent rule). Each user can give his opinion about a given situation rule. The most popular situations for the user's current locations and role could be recommended. The popularity of a situation rule is calculated using the following formula:  
\[PS_{i}=\frac{\sum_{j=1}^{N}Rating\_S_{i}^{j}}{N} \tag{1}\]  
where:  
* \(PS_{i}\): the popularity of situation \(i\).
* \(Rating\_S_{i}^{U_{i}}\): the rating of user \(j\) for situation \(i\).
* \(N\): the number of users rated the situation \(i\).  
**Step 4--Storage of situation rules**. Each situation rule is stored in their appropriate class.
# Reliability-Based View Synthesis for Free Viewpoint Video
## 2 Related Work
In [20; 21], a background sprite was generated by the original texture and synthesized images from the temporal previous frames using disocclusion filling, but the temporal consistency of the synthesized images needs further investigation, as described in [22]. In [23], [PERSON] proposed a disocclusion filling approach based on temporal correlation and depth information. Experimental results showed that this approach yields better subjective and objective performance beyond the above-mentioned spatial methods of filling disocclusions. However, the SVD format limits its wide usage because of the small baseline. Besides, some disocclusion regions that are not included in a single reference view may easily be spotted in another virtual viewpoint, and reverse mapping [4] may be more reliable. In [23], [PERSON] and [PERSON] et al. proposed the use of a constructed background video with a modified Gaussian mixture model (GMM) to eliminate the holes in synthesized video. The foreground objects are detected and removed, then motion compensation and modified GMMs are applied to construct a stable background. Results indicated that a clean background without artifacts of foreground objects can be generated by using the proposed background model, so that the blurry effect or artifacts in disoccluded regions can be eliminated and the sharp edges along the foreground boundaries can be preserved with realistic appearance [24]. Although [17] indicates that large holes in a target virtual view can be greatly reduced by using other more neighboring complementary views in addition to the two (commonly used) most neighboring primary views, we still employ only two reference views to render virtual views in our proposed framework. The occlusions that appear on one warped image will be filled by another reference viewpoint in the weighted blending process. In this paper, a multiview plus depth (MVD) format is employed for view synthesis. Two reference views are selected to interpolate virtual views located between them. Occlusions that appear on one warped image will be filled by another reference viewpoint. In addition, an adaptive background modeling method is proposed to construct background intensity distribution. The stable constructed reference background image helps to fill the remaining unfilled regions that are left due to the unreliable depth map. Another novelty of the proposed algorithm relates to depth refinement, which has the advantage of eliminating some noise caused by the coarse depth map. We also present a weighted blending process to blend two warped images from reference views based on the reliability of each pixel. An adaptive median filter and a depth map processing method are utilized before generating the synthesized virtual image.
## 2 Related Work
### Task Offloading Model
[14] proposed a new blockchain-based secure task offloading framework for MEC systems with corresponding improvements in execution latency and energy consumption, as well as a deep reinforcement learning-based task offloading decision algorithm to efficiently derive near-optimal task offloading decisions to address the challenges of dimensionality and dynamic nature of the scenario. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Research Focus** & **Research Method** & **Main Contribution** & **Differences from Other Papers** \\ \hline \multirow{8}{*}{[10]} & Rate maximization for wireless powered mobile-edge computing & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & This paper uses meta reinforcement learning to handle more complex task offloading decision-making problems \\ \cline{1-1}  & & & & decision-making problems \\ \hline \multirow{4}{*}{[11]} & Computation offloading based on cooperation among mobile edge computing-enabled base stations & Base station cooperation & Minimizing energy consumption & This paper uses meta reinforcement learning to adaptively make task offloading decisions \\ \cline{1-1}  & Code-partitioning offloading schemes for augmented reality in mobile edge computing & Code partitioning offloading & Minimizing latency & This paper uses meta reinforcement learning to handle different types of tasks and environments \\ \hline \multirow{4}{*}{[14]} & Secure task offloading in blockchain-enabled mobile edge computing with deep reinforcement learning & Blockchain, deep reinforcement learning & Secure task offloading & This paper uses meta reinforcement learning to dynamically adjust task offloading strategies \\ \cline{1-1}  & Task offloading strategy for IoT devices in an edge cloud computing environment & Meta reinforcement learning & Improving task processing speed & This paper adopts a meta reinforcement learning approach to adaptively make task offloading decisions considering multiple factors, such as energy consumption, latency, and task types \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of differences in Internet of things task processing approaches.
# Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring
## 2 Materials and Methods
### Physical Analysis of Differential Signals  
The wireless wearable device incorporates two accelerometers, one located near the laryngeal prominence (LP) and the other on the sternal manubrium (SM), to capture biometric vibration signals. During body movement, both accelerometers measure acceleration. While walking or experiencing overall body shaking, the acceleration value may be affected. However, since both accelerometers are impacted, the utilization of a differential signal, derived from the variance between the signals detected by the two accelerometers, enables the removal of acceleration attributed to overall movement.  
Figure 1 shows a schematic diagram of body changes and the location of the accelerometer during the exhalation phase of breathing. The acceleration utilized in Figure 1 is the acceleration perpendicular to the attachment surface of the device. In Figure 1a, the change in sternum position corresponding to lung volume alteration during exhalation is depicted. During normal exhalation, lung volume decreases, causing the angle between the SM and near the LP to diminish. The gray dotted line represents the SM's normal position, while the solid black line depicts its position during exhalation. Figure 1b presents a schematic diagram replicating the SM's position from Figure 1a. Here, let us denote the angle between LP and SM as \(\theta\) and estimate the actual acceleration measured by the accelerometer. Both accelerometers gauge acceleration resulting from overall body movement and the angle \(\theta\) between them. If the acceleration in LP is denoted as \(a_{LP}\) and the acceleration in SM as \(a_{SM}\), the differential signal, i.e., the difference between the two signals, can be expressed as Equation (1).  
\[a_{LP}-a_{SM}=f+g\sin\theta \tag{1}\]  
Figure 1: Physical changes during the exhalation phase of respiration. (**a**) Schematic diagram of sternal manubrium (SM) changes; (**b**) schematic diagram with vectors.  
Therefore, upon obtaining the differential signal, the accelerometer measures \(-\sin\theta\), influenced by external forces (\(f\)), gravitational acceleration, and the angle \(\theta\). During exhalation, as the angle \(\theta\) decreases, the \(\sin\theta\) value also decreases, resulting in a decrease in the measured acceleration value. Conversely, during inhalation, as \(\theta\) increases, the \(\sin\theta\) value also increases.
# Modelling Regime Changes of Dunes to Upper-Stage Plane Bed in Flumes and in Rivers
## 3 Step Length Models from Literature
### [PERSON] et al. (2009) Step Length Models  
[PERSON] et al. (2009) [14] have used a minimum (\(\alpha_{min}\) = 50) and maximum (\(\alpha_{max}\) = 250) value of non-dimensional step length \(\alpha\) in a conceptually derived relation between \(\alpha\) and dimensionless grain shear stress \(\theta^{\prime}\). For values of \(\theta^{\prime}\) between 0 and 0.5 (the dune regime), \(\alpha\) was assumed to be constant at the minimum value (\(\alpha_{min}\)). For values of \(\theta^{\prime}\) above 0.8 (the upper-stage plane bed regime), \(\alpha\) was assumed to be at the maximum value (\(\alpha_{max}\)). In the transitional regime (\(\theta^{\prime}\) from 0.5 to 0.8), \(\alpha\) was linearly interpolated. Besides the Shields parameter, there is no further dependency on sediment parameters.