esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 QoS Self-Provisioning and Interference Management Concept
### Step 4--Downlink Parameter Verification  
Finally, the total transmit power configuration of the HNB must satisfy the following inequality  
\[P_{HSDSCH}^{opt}+P_{CPICH}^{opt}+P_{common-CH}\leq P_{HNB-MaxTX} \tag{15}\]  
where \(P_{common-CH}\) is the power of the common control channels and \(P_{HNB-MaxTX}\) is the hardware limitation on maximum HNB transmission power. If Equation (16) is not satisfied the femtocell coverage is reduced according to Equation (11). The new configuration requires execution of algorithm with new configuration parameters from Equation (1).  
The end-user required uplink or downlink throughput is not guaranteed in the entire apartment/house if Equations (10), (12) and (15) are not all fulfilled in the first iteration of the proposed QoS-SP-IM algorithm. If Equations (10), (12) and (15) are not fulfilled the femtocell range was reduced during the execution of the algorithm. However, from an operator's point of view, the macro performance in the surrounding macrocells is preserved and is not degraded due to dense HNB deployment but at the cost of higher outage in femtocell performance. Tradeoffs are shown in the next section.
## 3 Results
### Technical Performance Results
#### 3.1.2 Calorimetry  
The specific heat flow and cumulative heat of the pastes produced with and without slag in the first 72 h and up to 28 days are shown in Figure 4a,b, respectively. It was observed that the heat flow normalised per gram of cement in the case of P_PC was similar to the pastes blended with slag up to 15 h from the time of mixing, after which the mixtures incorporating slag showed a higher rate of heat flow. The mixture containing quartz powder also showed a higher heat flow rate compared to the pure cement paste at all times and also compared to the slag-blended pastes up to the age of 3 days (except for short time intervals where it falls below). There was no significant shift in the heat flow peak times of the slag-blended mixtures in either direction compared to that of Portland cement. In the case of the quartz-blended mixture, however, the peak time slightly shifted to the left (indicating faster early-age hydration reactions), which could be the result of the nucleation effect of quartz powder or the higher access of the cement to water (higher w/c ratio compared to the control), which could expedite the hydration. In other words, since unlike the slags, the quartz had no retarding effect, the heat flow rate peaked at an earlier time. Moreover, the flow rates were higher in the first 11 h in the case of the mixture containing quartz powder compared to slag-blended mixtures. Similar retardation effects were reported by [PERSON] et al. for copper slag [PERSON] et al. (2016). Nonetheless, the heat release behaviour of all mixtures containing slags were similar and close to that of the mixture containing quartz, and no significant difference in the cumulative heat flow of such mixtures was observed up to 28 days (see Figure 4). This also signifies the poor pozzolanic reactivity of the treated NFM slags compared to other SCMs such as GGBFS, silica fume and calcined clays.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Slag Source**} & **3-Day Heat** & **7-Day Heat** & **7-Day Bound Water** \\  & **(J/g SCM)** & **(J/g SCM)** & **(g/100 g Dried Paste)** \\ \hline TS1: Treated S1 & 58.0 & 104 & 3.2 \\ TS2: Treated S2 & 58.0 & 88.0 & 2.3 \\ TS3: Treated S3 (synthetic) & 73.0 & 127 & 3.3 \\ \hline Some conventional SCMs & & & \\ \hline Blast furnace slag & 460 & 518 & 8.5 \\ Fly ash & 104 & 173 & 4.5 \\ Calcined clay & 527 & 574 & 10.4 \\ \hline Inert material & & & \\ \hline Quartz & 16 & 21 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: R3 test results for the slags, along with some conventional SCMs and inert quartz.  
Figure 3: Heat release patterns of the treated slags and quartz as per the R3 test.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 5 Channel Model and BER Analysis
### LN Channel  
When a channel is affected by a weak-moderate turbulence, it can be modeled using the LN. The PDF of LN channels can be defined as [53]:  
\[f_{h}(h)=\frac{1}{2h\sigma\sqrt{2\pi}}\exp\bigg{(}-\frac{(\ln(h)-2\mu)^{2}}{8 \sigma^{2}}\bigg{)}, \tag{10}\]  
where \(h=\exp{(2X)}\) is the channel irradiance from the transmitter to the receiver, with \(X\) modeled as an identically distributed Gaussian random variable with a mean \(\mu\) and a variance \(\sigma^{2}\). The average BER (\(P_{e}\)) for a M-ary pulse amplitude modulation (M-ary PAM) over a LN channel can be defined as [55]  
\[\begin{split} P_{e}&\approx\frac{G}{12}\sum_{i=1}^{ n}w_{i}exp\bigg{(}-\frac{log_{2}(M)a\overline{\gamma}e^{-4\sigma^{2}+x_{i} \sqrt{32\sigma^{2}}}}{4(M-1)^{2}}\bigg{)}\\ &+\frac{G}{4}\sum_{i=1}^{n}w_{i}exp\bigg{(}-\frac{log_{2}(M)a \overline{\gamma}e^{-4\sigma^{2}+x_{i}\mu\sqrt{2}}}{3(M-1)^{2}}\bigg{)}\end{split} \tag{11}\]  
where \(G=\frac{2(M-1)}{Mlog_{2}(M)\sqrt{\pi}}\), \(x_{i}\) and \(w_{i}\) are the zeros and weights of the Hermite polynomial [56] of order \(n\), respectively. An existing study [57] demonstrated that accurate results can be obtained when \(n\geqslant 20\) and \(\overline{\gamma}\) is the average SNR, with \(a=(w(L)/w_{eff}(L))^{2}\) where \(a\) is the loss at the beam center.  
The outage probability of the proposed system over log-normal channel can be calculated using [58]  
\[P_{out}=Q\bigg{(}\frac{\ln(\sqrt{a}P_{M})+2\mu}{2\sigma}\bigg{)}. \tag{12}\]where \(P_{M}=\sqrt{\frac{\gamma}{\gamma_{th}}}\) is the power margin and \(\gamma_{th}\) is the minimum signal-to-noise ratio required for no outage to occur and the signal can be decoded with an arbitrarily low error probability at the receiver.  
The availability of the system can be calculated as follows  
\[Availability(\%)=(1-P_{out})\times 100. \tag{13}\]
## 3 MPPT Techniques  
The MPPT technique is an electronic system that provides the duty cycle to the power conversion device in order to achieve peak power generation. As observed in Figure 6, MPPT is also well-known as an MPP matching device that is positioned between the PV array and the power converter [92; 93; 94]. Different inverter systems, external grids, battery banks, and other electrical loads interact differently with solar PV systems. The main issue that MPPT attempts to solve is the fact that the efficiency of power transmission from the solar cell is dependent on the quantity of sunlight, the amount of shade, the temperature of the solar panel, and the electrical characteristics of the load. The load characteristic impedance provides the maximum power transmission changes when these variables  
Figure 4: Solar PV Characteristics for (**a**) different values of irradiances and (**b**) different values of temperature.  
Figure 5: The P-V curves under various shading conditions.  
change. To maintain the best efficiency of power transfer as the load characteristic changes, the system is optimized. The maximum power point refers to this ideal load property (MPP). MPPT is the process of adjusting the load characteristic as the conditions change. Circuits can be designed to present optimal loads to the photovoltaic cells and then convert the voltage, current, or frequency to suit other devices or systems. The MPP tracking starts with an initial duty cycle. The input current \(I_{PV}\) and voltage \(V_{PV}\) of the boost converter are measured to calculate the SPVS power \(P_{PV}\) (\(k\)). Now, based on initial changes in power, the controller increases the duty cycle. At this stage, new \(I_{PV}\) and \(V_{PV}\) are measured to calculate new power \(P_{PV}\) (\(k+1\)). Based on present and past information on SPVS power, the controller decides to decrease or increase the duty cycle. This process of tracking is continuous until the MPP is reached.  
However, there are other important considerations when constructing MPPT techniques for PV systems, including efficiency, kind of implementation, complexity, lost energy, and cost [95]. Based on these, various MPPT approaches have been created and classified into three main groups, namely (i) offline or indirect MPPT techniques; (ii) online or direct MPPT techniques; (iii) intelligent MPPT techniques. A broad classification of MPPT techniques is shown in Figure 7.
## 3 Results
### Spatial Patterns of Thermal Discharge in Response to Varying Tidal Conditions  
The thermal plumes released from the west side of the plant provide valuable insights into surface water current patterns. Figure 4 illustrates the spatial distribution of the thermal discharge water from the HYNPP under various tidal conditions.  
During the flood tide (Figure 4a-c), the southwestward current propels the warm plume along the water flow direction, forming a distinct thermal boundary between the warm and cold water masses. As the flooding current intensifies, the warm plume is gradually confined to the bay near the outlet, and the warm water is pushed towards the nearshore region. Near high tide (Figure 4c), the warm plume is fully contained within the bay, and the warm water tends to extend along the nearshore zone.  
During the ebb tide (Figure 4d,e), the northeesterly current carries the warm plume out of the bay. The combined effects of falling currents and coastal topography compress the plume, forming a narrow strip. As the falling tide intensifies (Figure 4f), the warm plume breaks through the headland and spreads northeastward, exhibiting a similar pattern to the initial stage of the flood tide cycle.  
The spatial distribution and morphological changes of the thermal plume are thus closely linked to tidal cycles [34]. The detected surface plume location is directly influenced by the prevailing flow direction under different tidal conditions [26].
## 3 Results and Discussion
### Impact Indent Geometry
The linear rate of the width for the pyramidal and hemispherical indents was calculated as 0.65 and 0.2 mm/J, respectively. The width, \(\rm w_{max}\), for UD laminates was within a range of between 3.82 and 6.45 mm. Figure 3: Typical shape and profile of the pyramidal and hemispherical impact indents. Figure 4: Maximum depth and width of the pyramidal and hemispherical indent (**a**) for the TRI (**b**) UD GFRP laminates. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Laminate Type** & **TRI** & \multicolumn{2}{c}{**UD**} \\ \hline \hline \multirow{2}{*}{**Impact Energy (J)**} & **Head Shape** & **Hemispherical** & **Pyramidal** & **Hemispherical** & **Pyramidal** \\ \cline{2-6}  & 4 & \(4.64\pm 0.25\) & \(4.39\pm 0.15\) & \(4.36\pm 0.09\) & \(3.82\pm 0.55\) \\ \cline{2-6}  & 6 & \(5.27\pm 0.06\) & \(5.05\pm 0.3\) & \(4.93\pm 0.16\) & \(4.84\pm 0.8\) \\ \cline{2-6}  & 8 & \(5.56\pm 0.09\) & \(5.94\pm 0.56\) & \(5.07\pm 0.18\) & \(6.45\pm 0.7\) \\ \hline \multirow{2}{*}{Depth, z\({}_{\text{max}}\) (\(\mu\)m)} & 4 & \(45.6\pm 1.6\) & \(110.9\pm 15.5\) & \(63.3\pm 4.9\) & \(163.8\pm 6.7\) \\ \cline{2-6}  & 4 & \(59.6\pm 6.9\) & \(113\pm 18.9\) & \(80.9\pm 12.6\) & \(172.4\pm 10.2\) \\ \cline{2-6}  & 8 & \(77.2\pm 10.6\) & \(117.3\pm 18.5\) & \(101.7\pm 5.5\) & \(206.8\pm 5.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimension of impact indent profiles created in the UD and TRI laminates.
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 3 Experiments and Results  
### Experiments  
#### 3.1.1 Image Selection and Annotation  
Given the variability in contrast agent distribution during the initial and final phases of angiography, which impacts vascular visualization, we meticulously selected 250 representative images from the dataset for model development. Experienced radiologists from Hokkaido University Hospital provided diagnoses for these patients, facilitating precise image annotations, particularly focusing on areas with over 50% stenosis due to their higher risk and clearer visibility.
## 3 Results and Discussion
### Dataset for Conducting Experiments  
#### 3.1.1 Facial Expression Recognition Dataset  
The Facial Expression Recognition (FER+) [46] dataset extends the original FER2013 dataset. This dataset is designed for building and testing facial emotion recognition models. The full FER+ dataset contains more than 35,000 grayscale images, each 48 \(\times\) 48 pixels in size, and reflects the results of a large-scale study of human emotions. FER+ differs from FER2013 because it has increased annotation reliability with multiple independent labels for each image, effectively reducing annotation noise.  
In this study, a small set of FER+small images with five basic human emotional states was compiled to test the proposed IT: Anger, Fear, Joy, Neutral, and Sadness. The total number of images in FER+small is 6236. Each category of emotions in the selected dataset has a different number of samples, which reflect real-life facial expressions.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Metal or Non-Metal Ion Doping
#### 5.3.1 Metal Ion Doping
They found that the atomic number and concentration of the lanthanide influence the band gap energy and the specific surface area of the catalyst but have little influence on visible light absorption. [PERSON] et al. [281] prepared TiO\({}_{2}\) semiconductor photocatalysts with Fe\({}^{3+}\), Cr\({}^{3+}\), La\({}^{3+}\), and Eu\({}^{3+}\) ions doped and co-doped by a sol-gel method. In the experiment of decomposing p-nitrophenol under ultraviolet and visible light, the La-Fe-TiO\({}_{2}\) catalyst showed better photocatalytic ability than the other samples.
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
###### Abstract
In [9], an eight-element MIMO antenna for the sub-6 GHz LTE band 42 was presented. The antenna elements were printed on the side-edge frame of the smartphone. To achieve high isolation, a decoupling structure was designed between the antenna elements. The designed MIMO antenna offers an impedance bandwidth of 300 MHz from 3.3 GHz to 3.6 GHz. Furthermore, the addition of the decoupling structure excited a new resonance in the frequency range of 2.4-2.7 GHz. A dual-band eight-element planar MIMO antenna was presented in [10] for sub-6 GHz applications. The dual-band resonance was achieved by using an L-shaped slot on the ground plane. Similarly, in [11; 12], dual-band MIMO antenna systems were designed for LTE bands 41 and 42, respectively. In [13], an eight-element MIMO antenna was presented with a chip capacitive decoupler. The resonance bandwidth of the antenna varied over 3.33-3.69 GHz, and the efficiency was found to be \(>\)50% at 3.5 GHz. In [14], a dual-band antenna operating at 3.38-3.82 GHz and 4.75-5.13 GHz was presented. The MIMO system showed an isolation value of 14 dB with the insertion of a decoupling structure. In [15], an eight-element MIMO antenna was designed, which comprised an inverted S-shaped resonating strip with an inverted L-shaped radiator mounted on the side-edged frame of the smartphone. The designed antenna exhibits dual-band response ranging over 3.3-3.8 and 4.9-5.7 GHz. The isolation between the MIMO elements was noted to be 11 dB throughout the resonance bandwidth. The MIMO antennas discussed above offer acceptable performance, but they suffer due to their design configuration. Accommodating a huge number of antennas with other electronic components can be tricky and hard to accomplish. Therefore, in this paper, an eight-element MIMO antenna system is presented for dual-band sub-6 GHz applications. The antenna elements are designed on the side-edge frame of the smartphone to provide space for other components used in mobile phone assembly. From the presented results, it is observed that the designed MIMO antenna resonates at two distinct 5G allotted bands, i.e., 3.4-3.6 GHz and 5.1-5.8 GHz. The isolation between the antenna elements is noted to be \(>\)18 dB for the 3.5 GHz frequency band and \(>\)12 dB for the 5.4 GHz frequency band. Furthermore, good diversity performance is observed for the proposed MIMO antenna, which makes it suitable for future 5G-enabled smartphone applications.
# An Uncertainty-Driven Proactive Self-Healing Model for
## 2 Preliminaries and Scenario Description
### Trend Estimation  
Let us consider discrete time intervals where we record the inter-arrival rate of tasks and their execution rate. Obviously, if \(\lambda>>\mu\), \(G_{i}\) is not stable, it will certainly be overloaded. However, in real setups, we can observe fluctuations in the realization of \(\lambda\) and \(\mu\). Hence, we decide to focus on every discrete time interval (e.g., minutes, hours, days, etc.) and deal with the rate of execution \(\mu\) as a time series dataset. Our target is to check the trend of the execution rate in order to support the decision making concerning the offloading of tasks. In simple words, if we see that \(\mu\) increases, we can postpone the offloading of tasks to eliminate the time required for sending tasks to peers and obtaining the final results to deliver them to the requestor. For instance, new tasks placed for execution may request simple processing activities or actions that can be based on previous results; thus, their re-use can significantly reduce the execution time. We rely on the time series \(\{\mu_{1},\mu_{2},\ldots\}\) where \(\mu_{j}\) is the execution rate at each discrete interval and apply a simple yet powerful technique for extracting the trend of the execution rate. This technique is [PERSON]'s tau statistic [26, 27]. [PERSON]'s coefficient (\(\tau\)) is a non-parametric statistical method that is adopted to measure the correlation and the relationship strength between two measured quantities. This coefficient is applied on the aforementioned \(\mu\) time series \(\{\mu_{1},\mu_{2},\ldots\}\). The \(\tau\) value is bounded in \([-1,1]\) and calculated with the following equation:  
\[\tau=\frac{S}{\binom{n}{2}} \tag{9}\]  
where \(n\) is the number of \(\mu\) upon which \(\tau\) is applied and \(S\) depicts the Kendall Score which is computed as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign(\mu_{j}-\mu_{i}) \tag{10}\]  
\[sign(\mu_{j}-\mu_{i})=\begin{cases}1,&\mu_{j}-\mu_{i}>0\\ 0,&\mu_{j}-\mu_{i}=0\\ -1,&\mu_{j}-\mu_{i}<0\end{cases} \tag{11}\]  
The closer to \(+1\) the value of \(\tau\) is, the more increasing the trend becomes, while the most decreasing trend is achieved when \(\tau\) is very close to \(-1\). The data have no trend through time when \(\tau\) does not significantly differ from zero. In our model, we normalize \(\tau\) in the unity interval (\([0,1]\)).
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 4 Antenna Feeding for Fabric Antennas
### Aperture-Coupled Feeding with Coaxial Line  
Figure 7 shows the conceptual views of the feeding technique with a coaxial cable mounted on a fabric patch antenna. Two antenna models are designed with the feeding technique at BLE and LoRa frequency bands. As stated in Section I, both technologies are ideal for long-range on-body mobile communication with low energy consumption support for IoT devices.  
Similar to a regular patch antenna, a 3 mm felt fabric (\(\varepsilon\)r = 1.36, tan \(\delta\) = 0.02.) is sandwiched between the radiating patch and ground layers, which are both made from conductive printed fabric, elaborated in Section II-B. The aperture slot splits the ground layer into two alternating sections when current is applied to the cable, enabling the aperture to act as a resonating electromagnetic gap at the given frequency of operation. In this regard, the aperture length (\(l\)) is approximately \(\lambda\)g/4 (\(\approx\)72 mm @ 868 MHz/\(\approx\) 26 mm @ 2.44 GHz), thus making the aperture a quarter-wave dipole slot with maximum voltage amplitude at either end of the aperture. The narrow slot creates a high impedance traveling current towards the aperture ends. This phenomenon in turn drives the current upwards to the low impedance radiating patch, hence the coupling. When performed optimally, this technique results in lower back lobe radiation, which is a critical drawback to the aperture feeding method.  
Figure 7: Conceptual views of the coaxial aperture-feeding technique (see Table 2 for dimensions).  
Unlike traditional patch antennas, the radiating patch width (_Wpatch_) shown in Figure 7 is optimized to the same width as the ground width (_Wground_). This is performed to ensure optimum coupling from the end sections of the aperture slot to the radiating patch. In this instance, a trade-off is made between maximum coupling for back lobe reduction and fringing currents required for the _yz_ plane radiation pattern. Fringing currents at the patch edges (_yz_ plane) are optimized with the use of the thicker 3 mm substrate and an optimized slot length. The patch lengths are optimized at \(\uplg\)/2 of the operating center frequency. Table 2 shows the optimized parameters.
## 4 Nanostructured Materials for Uranium Removal
### Application of a Composite Material (GO-DTPAA) for U(VI) Removal  
A novel chelator diethyl-enetri-amine-penta-acetic phenyl-enedi-amine (DTPAA) was covalently bonded to a supporting matrix of graphene oxide (GO), and a composite material (GO-DTPAA) was obtained recently by [PERSON] et al. (2018) [85]. The obtained results indicated that GO-DTPAA was a highly efficient absorbent for the removal of U(VI) from aqueous solutions at pH 6.5. The adsorption capacity of GO-DTPAA was as high as 485 mg g\({}^{-1}\) at 298 K, which was far greater than that of pristine GO (97 mg g\({}^{-1}\)) at the same temperature. The thermodynamic parameters revealed that the adsorption of uranium ions onto the pristine GO and by the GO-DTPAA composite material are feasible, spontaneous and endothermic.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 2 Preliminaries
### Artificial Neural Network  
An Artificial Neural Network (ANN) is a machine learning model inspired by the networks of biological neurons found in animals' brains. It is powerful, scalable, and versatile. Due to its characteristics, it can be collocated with other approaches to tackle large and highly complex tasks, e.g., classifying images, speech recognition, recommending videos, and so on [13].  
One of the simplest ANN architectures is Perceptron, which is based on artificial neurons. For each neuron, the inputs and output are numbers, and each input is associated with a weight and an extra bias feature may be added. Then, the weighted sum is applied to an activation function to output the result. Furthermore, Multi-Layer Perceptron (MLP) is an improvement of Perceptron. It is composed of one input layer, one or more hidden layers constructed from the concept of Perceptron, and one output layer. Thus, every layer except the output layer is fully connected to the next layer [13]. The computation of Perceptron is shown in the following equations:  
\[y_{i}=x_{n}w_{n}+x_{n-1}w_{n-1}+\ldots+x_{1}w_{1}+x_{0}w_{0}+bias_{i}, \tag{3}\]  
\[z_{i}=act_{i}(y_{i}), \tag{4}\]  
where \(x_{i}\) is the input, \(w_{i}\) is the weight of \(x_{i}\), \(bias_{i}\) is the bias, \(y_{i}\) the weighted sum, and \(z_{i}\) the output produced from the activation function \(act_{i}()\).  
Here,positioning topic is not a linear or simple polynomial issue due to the effects of signal attenuation and multipath propagation [14; 15]. Hence, this is another motivation for us to design a new architecture including ANN to refine the distance information further.