esa-sceva/satcom-chunk-collection huggingface dataset:
# Specific Mutations in Aph1 Cause \(\gamma\)-Secretase Activation
###### Abstract  
Amyloid beta peptides (ABs) are generated from amyloid precursor protein (APP) through multiple cleavage steps mediated by \(\gamma\)-secretase, including endoproteolysis and carboxypeptidase-like trimming. The generation of neurotoxic A\(\beta\)42/43 species is enhanced by familial Alzheimer's disease (FAD) mutations within the catalytic subunit of \(\gamma\)-secretase, presenilin 1 (PS1). FAD mutations of PSI cause partial loss-of-function and decrease the cleavage activity. Activating mutations, which have the opposite effect of FAD mutations, are important for studying A\(\beta\) production. Aph1 is a regulatory subunit of \(\gamma\)-secretase; it is presumed to function as a scaffold of the complex. In this study, we identified Aph1 mutations that are active in the absence of nicastrin (NCT) using a yeast \(\gamma\)-secretase assay. We analyzed these Aph1 mutations in the presence of NCT; we found that the L30F/T164A mutation is activating. When introduced in mouse embryonic fibroblasts, the mutation enhanced cleavage. The Aph1 mutants produced more short and long ABs than did the wild-type Aph1, without an apparent modulatory function. The mutants did not change the amount of \(\gamma\)-secretase complex, suggesting that L30F/T164A enhances catalytic activity. Our results provide insights into the regulatory function of Aph1 in \(\gamma\)-secretase activity.  
Alzheimer's disease; amyloid beta (A\(\beta\)); gamma-secretase; Aph1; intramembrane proteolysis; _Saccharomyces cerevisiae_
## 4 Implementation and Experimental Results
### Stream Mining Configuration  
The configuration of the EEG stream included the aggregation of each 256 rows to one single row because all rows related to a single second lead to either the normal or seizure class. This will reduce the required computational resources. Then, the 10 files were merged, in which the model will receive 10 rows from 10 patients every second. The parameters in scikit-multiflow were set as follows: sample size-frequency, which refers to the default window size, was set to 100; ensemble size was 11 base learners; info gain was chosen as the split criterion for building the random tree classifier; and split confidence was set to 0.01.
## 1 Introduction
### Deep Learning  
Deep learning is an emerging tool in wireless communications to solve numerical optimization problems while addressing time and complexity challenges. It has been proven to be the fastest solution in comparison to the traditional numerical optimization approaches [2]. Deep learning is a process which learns the input and output relation of any signal processing task. Multi-layer neural networks, called deep neural networks (DNN), can be used to approximate a given iterative optimization algorithm. The key idea is to consider the given algorithm as a \"black box\" and try to learn its input/output relationship by using a deep neural network. A network with several layers can effectively approximate a given algorithm by designing and formulating its internal structure. This can be computationally efficient and can reduce processing time for iterative algorithms as it requires multiple layers of simple operations such as matrix-vector multiplication [10]. A single-layer neural network can also provide an approximation to any function, but the layer may not be large enough and may fail to learn and generalize the model correctly. Thus, in many cases, using deep networks can reduce the number of hidden neurons to approximate a function and provide faster solutions [10; 11].  
The DNN consists of one input layer, one or more hidden layers and one output layer, as shown in Figure 1.The layers close to the input layer are usually called the lower layers, and the ones close to the output are usually called the upper layers. Every layer except the output layer includes a bias neuron and is fully connected to the next layer. A DNN contains a deep stack of hidden layers that makes the network architecture deep [11]. Each layer consists of neurons, which is where functions take place. The input layer consists of neurons equal to the number of input parameters or the number of features of the system. These neurons give an output from whatever input is fed into them. A DNN mainly describes the mapping of an input vector to an output vector. Hidden layers of DNN are designed to model the mapping of input and output vectors of the system.  
Neurons in hidden layers, and the number of hidden layers, are hyperparameters in the DNN, and it is necessary to adjust them for the accurate mapping of inputs and outputs. The output of every \(l\)th layer in the hidden layer is  
\[q_{l}=g(w_{l}q_{l-1}+b_{l}) \tag{1}\]  
where \(w_{l}\) and \(b_{l}\) are the weights and biases of the \(l\)th layer, respectively. Learning is the problem of finding the weights, \(\mathbf{w}\), within some feasible set that will lead to the output that describes the best mapping of the input; \(g(.)\) indicates an activation function that applies to the output of every hidden layer.
# Enhancing Furniture Manufacturing with 3D Scanning
## 1 Introduction  
The 3D scanning technology emerged in the 1960s [1]. However, its initial adoption was limited due to high hardware costs, low-quality scans, and difficulties processing large files. Today, 3D scanning has significantly advanced, finding applications from science and industry to creating digital characters for video games and movies and even archaeological and cultural heritage preservation through digitalization.  
In the industry, 3D scanning is a tool for product design and development, manufacturing processes, and quality control. Its applications extend even further, providing valuable information on deformation, displacement, and movement analysis [2; 3; 4; 5].  
In the furniture industry, constantly creating products with complex shapes and forms presents a challenge for engineers, especially when dealing with prototypes of designerconcepts [6]. The prototyping, from concept to final product, often involves multiple iterations due to difficulties in translating the designer's vision into a physical form [7; 8]. Three-dimensional scanning offers a significant improvement. After the designer creates a full-size, hand-crafted concept design, engineers can use 3D scanning technology to capture a digital copy. This digital model can be used to create a CAD model for future manufacturing [9]. There is a research gap in knowledge about the usefulness of 3D scanning in all stages of furniture production.  
This study employs a two-fold approach to evaluate the effectiveness of 3D scanning in furniture production, including a literature survey and an analysis of four real-world case studies. All case studies stem from collaborations between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering, University of Sarajevo, and furniture manufacturing companies in Bosnia and Herzegovina.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Sparse Jamming Estimation
In their study, it was shown that the premeasurement noise model can be reformulated into a general SSR model (i.e., measurement noise model), with a small cost on RIP. Based on these studies, we can conclude that \(\widehat{\mathbf{e}}\) can be successfully recovered, as \(\widehat{\mathbf{e}}\) also satisfies the soft sparsity model. To derive the SJE algorithms, we explored two representative SSR algorithms: greedy algorithms and sparse Bayesian learning (SBL) algorithms. In Section 3.4.1, we propose the GSJE algorithm. In Section 3.4.2, we propose the BSJE algorithm.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.1 Spatial Variability
Even though a general trend seems to confirm that the flat areas allow the maximum VIs mean values to be reached while the sloped terrain boosts the data dispersion, the interaction between topography and location could mix the two effects. The VIs from hilly and sloped vineyards can reach average CV values close to flat ones. Thus, sloped terrain can overcome hilly environmental issues and climate issues by taking advantage of suitable sunlight exposure.
## 3 Results and Discussion
### Using Spectra for Detection
At 650 nm, the brightest area of the dropping and the dirt clad were both saturated; however, the relative extent of the area of saturation for the dropping suggested that the dropping was brighter than the dirt clad. In any case, if the images were being reviewed by a human observer, the observer should easily be able to distinguish between the dirt clad and the dropping. This 650-nm image had one particularity that impacted detection under ambient conditions--there was a shadow passing across the image with the dropping in the shadow while the dirt clad had full sun exposure. This occurrence emphasized the potential difficulty of using measurements taken under real world conditions for detection. Spectral characteristics are not the only factors that should be considered when designing an optical instrument to survey fields for bird droppings. Other factors to consider include size of bird droppings, actual time birds spend in fields, excretion traits, and probability of carrying pathogenic bacteria. Dove droppings are small, some 2-4 mm in diameter, and identifying the smaller droppings in low resolution images would be difficult, if not impossible. Both the d  
Figure 9: Reflectance images of a pull dropping at (**a**) 525 nm and (**b**) 650 nm. Note that the brightest areas of the circled dirt clad and dropping in the 650-nm image were both saturated; however, the larger relative extent of the saturated area for the dropping suggested that the dropping was brighter at its maximum than the dirt clad. found to be roosting outside of fields. Gull flocks were found in fields, commonly on the ground in open areas next to irrigation canals. Gulls were observed to excrete on take-off and were also observed to take-off over irrigation canals. Numerous droppings were found immediately next to the canals, and it was reasonable to assume some droppings landed in the canals. Gulls are known carriers of pathogenic _E coli._[8; 9; 10], and migrating gulls have been linked to increases in E. coli in municipal water sources [10; 11; 12; 13]. When this study was conducted, numerous flocks of gulls were seen. Per locals, the high level of gull presence was a recent phenomenon. The flocks were found almost exclusively in fields of roanine lettuce that were being harvested, which was an interesting observation given the _E. coli_ outbreak linked to consumption of roanine lettuce harvested in Yuma in March of 2018 [25]. More study is needed of birds as potential vectors of pathogen contamination of produce; however, existing knowledge concerning gulls suggests that development of optical instrumentation to detect gull droppings in particular would be warranted.
# Photocatalytic Phenol Degradation by Silica-Modified Titanium Dioxide
## 3 Results
### Bandgap Characterization of TiO\({}_{2}\)/SiO\({}_{2}\) Composites  
UV-vis analysis on the catalyst was used to determine the bandgap of the photocatalyst. TiO\({}_{2}\) P25 Degussaa has clear ultraviolet light absorption characteristics. Although not significant, there was a decrease in the bandgap for the synthesized composites compared to TiO\({}_{2}\) P25 Degussaa, indicating that SiO\({}_{2}\) gave a slight change to the electronic state of TiO\({}_{2}\). The optical bandgap energy (Eg) is obtained using the Tauc equation [40]:  
\[(\alpha ln)^{n}=\mathrm{A}(ln\
u-\mathrm{E}_{\mathrm{g}}), \tag{3}\]  
where \(\alpha\) is the absorption coefficient, \(ln\
u\) is the energy of the photons, and \(\mathrm{A}\) denotes the proportionality constant. The transition property is represented by \(n\), where \(n=1/2\) for the indirect bandgap allowed [40]. Bandgap energy is calculated by plotting \((\alpha ln\
u)^{1/2}\) vs. \(h\
u\) (Figure 5). The bandgap values of TiO\({}_{2}\) P25 Degussaa and TiO\({}_{2}\)/SiO\({}_{2}\) composites are shown in Table 7. TiO\({}_{2}\) P25 Degussaa has a bandgap value of 3.02 eV, where the TiO\({}_{2}\)/SiO\({}_{2}\) composites have a smaller bandgap, namely, 2.95 eV for TiO\({}_{2}\)/SiO2 1:1 and 2.96 eV for TiO\({}_{2}\)/SiO\({}_{2}\) 3:1 and 7:1.  
The difference in bandgap values can be explained in terms of changes in the energy of the TiO\({}_{2}\) bandgap due to SiO\({}_{2}\). The cause of this bandgap difference is possible through three mechanisms: (i) quantum size effect [33]; (ii) Ti-O-Si bond formation, leading to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Catalyst** & **Bandgap Energy (eV)** \\ \hline TiO\({}_{2}\) P25 Degussaa & 3.02 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (1:1) & 2.95 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (3:1) & 2.96 \\ TiO\({}_{2}\)/SiO\({}_{2}\) (7:1) & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The bandgap values of the photocatalyst.  
Figure 5: Tauc plot obtained through the application of Equation (3) for the photocatalyst of TiO\({}_{2}\) P25 Degussa and composite of TiO\({}_{2}\)/SiO\({}_{2}\).  
electronic structure modification [30]; and (iii) percentage difference between the anatase and rutile phases, where rutile has a different bandgap with anatase [41]. Figure 6 shows the energy band diagram of the TiO\({}_{2}\) and SiO\({}_{2}\)/TiO\({}_{2}\) composites. Due to the very large SiO\({}_{2}\) bandgap (8.6 eV) [42], electron heterojunction between the valence band and conduction band of TiO\({}_{2}\) and SiO\({}_{2}\) is difficult. Bandgap changes only occur due to Ti-O-Si bond formation.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 4 Experimental Results and Discussion
### Latency and Throughput Metrics  
In assessing the performance of TransIQ (large and small variant), two metrics of latency and throughput were analyzed as well. These analyses offer insights into the models' effectiveness and suitability for real-world scenarios. Latency is defined as the duration required for the model to process a single input, measured in seconds. On the other hand, throughput refers to the model's capability to process a certain number of inputs per second.  
Our experiments to measure latency and throughput were carried out using the NVIDIA Jetson AGX Orin Developer Kit, created by NVIDIA Corporation, headquartered in Santa Clara, California, United states. This kit is equipped with an Ampere architecture GPU with 2048 CUDA cores and is also powered by an ARM-based CPU with 12 cores. The system includes the memory of 64 GB, providing sufficient resources for demanding AI tasks. To carry out our measurements, we utilized the CPU resources provided by the kit, in the MAXN performance mode and on NVIDIA JetPack version 6.0-b52. The measured latency and throughput for the two variants of TransIQ are presented in Table 5. The table illustrates that the latency for TransIQ (small variant) is 3.36 ms/sample, while the large variant has a higher latency of 5.93 ms/sample. This increased latency for the large variant  
Figure 6: Confusion matrix for ResNet versus proposed models (TransIQ-large variant and TransIQ-small variant) on CSPB.ML.2018+ dataset. Each row represents the actual class, and each column corresponds to the class as predicted by the mentioned models.  
was anticipated due to its greater complexity, with 229 K parameters compared to the 179 K parameters of the small variant, requiring more time to make predictions. In terms of throughput, the small variant demonstrates a throughput of approximately 297 samples per second. This high rate indicates its capability to manage large volumes of predictions with minimal latency, making it well-suited for real-time applications with a bit lower accuracy. Conversely, the large variant has a throughput of approximately 168 samples per second. Although this is lower compared to the small variant, the large variant is suitable for AMR applications requiring higher prediction accuracy, at the cost of slower processing input data.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results
#### 4.1.3 Coverage Angle  
The coverage angle (\(\alpha\)) is a parameter of the drone that determines the area where a drone can facilitate effective communication. Our analysis shows that an increase in coverage angle reduces the number of drones required. However, this relationship is only sublinear, as shown in Figure 18d.
## 2 Materials and Methods
### Tar Sampling  
Some tar-sampling methods that have been improved in recent years are associated with the advantages and disadvantages of the system [16]. The famous method for getting tar sampling was executed in the liquid-cooled-solvent method using isopropanol [17]. In this experiment, tar sampling was carried out between the gasifier and soot remover, and the sampling line was shielded from the gasifier at 250 \({}^{\circ}\)C to avoid many impurities, including ash, dust, and soot. The heating process was executed before inserting the tar-sampling bottle. Ethylene glycol was used as an antifreeze to keep the tar cool in a Dewar vessel at -20 \({}^{\circ}\)C. The experiment equipment is shown in Figure 2. Three tar-sampling impinger bottles with a 300 mm length and 24 mm inner diameter respectively were used to collect tar in the syngas through a surface-contact method with 30 mL of glass beads (AS ONE BZ-2, diameter 1.5-2.5 mm). The syngas-flow rate was set to 8 L/min for 20 min for the intention of collecting 160 L of syngas with the tar-sampling equipment. The collection process was based on the standard tar sampling system conducted by tar protocol. The standard tar protocol used CEN/TS 143:2005 with modification to the number of impinger bottles and without liquid inside them. The following method was used: \"Method and Apparatus for Collecting Tar\" which has the patent number JP 2009-40885 since 2007 from the Japan Patent Office. This method was developed by [PERSON] and [PERSON] [13; 18]. The syngas from the gasifier was passed on the heated line and through the inlet pipe at the impinger bottles and the tar in the syngas was caught by making contact with the surface with 30 mL of glass beads. In the next process step, the syngas passed from the impinger bottle through the outlet pipe and this process occured continuously in impinger bottle numbers 1, 2, and 3. Then, the syngas was filtered using filter apparatus with a porosity diameter of 0.45 \(\upmu\)m. This filtering was executed in order to avoid impurities. When the impinger bottle was filled with syngas, the flow rate was set up to 8 L/min for 20 min for controlling the amount of syngas in the tar-sampling equipment. The flow rate was measured by the gas-flow meter which was metrologically tested.  
The tar in the syngas was extracted using acetone with 99.5% purity in 50 mL through surface contact with the glass beads. Pure acetone was injected into the tar-sampling bottle and was shaken at least fifteen times to ensure that the mixing of the acetone and glass beads was homogenous. A filter (SIMPLEPURE, NY 0.45 \(\upmu\)m) was used to remove the impurities such as soot, ash, and dust in the process-extract tar sample. All of the tar sample was kept in a 2 mL vial bottle for the next analysis of the compounds using gas chromatography - mass spectrometry (Shimadzu GC-MS QP2010 Plus, made by Shimadzu Corporation, Tokyo, Japan). Finally, all of the apparatus and the tar sampling bottle were cleaned using fresh acetone and an ultrasonic cleaning machine (AS ONE).  
Figure 1: The biomass gasification system.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 4 CFD Model  
In this work, a simplified CFD model is used to study the effects of convection and radiation on PV array temperature. We consider the PV array to be two dimensional, as the third (depth) dimension of the PV array is far larger than the other two (length and thickness). The length, \(L\), of the array is 3 m and it is 0.04 m in thickness, with a tilt angle  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Equipment** & **Accuracy** & **Sample Rate** \\ \hline RM Young 2D Ultrasonic Anemometer & \(\pm\)2\% & 0.1 Hz \\ Dycor DT85 with CEM20 Dattaker & \(\pm\)0.5\% & 0.1 Hz \\ Acquency Aucvim II Smatrter & \(\pm\)0.2\% & 0.1 Hz \\ [PERSON] \& Zonen CMP10 Pyronometer & \(\pm\)1.5\% & 0.1 Hz \\ [PERSON] \& Zonen CMP22 Pyronometer & \(\pm\)1.5\% & 0.1 Hz \\ Omega SA1 XL Thermocouple & \(\pm\)0.7 \({}^{\circ}\)C & 0.1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reported Equipment Error and Sampling information.  
of 45\({}^{\circ}\) from the x-direction. The domain is 133.3\(L\) in the streamwise direction (x-direction) and 50\(L\) in the vertical direction (y-direction). The PV array is placed 36.7\(L\) downstream from the inlet and 25\(L\) from the bottom boundary. The simulation domain is illustrated in Figure 2.  
For simplicity, the model does not consider the effects of neighboring structures. The inlet boundary condition is a uniform velocity, ranging from 1 to 9 m/s at 300 K. The pressure outlet boundary condition is imposed on the outlet surface, while the top and bottom boundary conditions are inlet velocities in the streamwise direction. The top surface of the PV array experiences a heat flux of 800 W/m\({}^{2}\). The present work was carried out using commercial CFD software, Ansys Fluent 2020, on a Dell Optiplex 780 desktop computer. We used the \(k-\varepsilon\) turbulence model with the Menter Lechner wall treatment, and the P \(-\) 1 radiation model is used with a blackbody temperature of 291 K. The coupled pressure-velocity coupling scheme was used, along with pressure discretization of PRESTO!, and QUICK scheme for momentum, turbulence, and energy. Using this CFD model, we studied temperature distribution as well as the thermal loss factor/heat transfer coefficient, which are both functions of wind speed. A mesh sensitivity study was performed, shown in Table 4. A total of four meshes were used, ranging from 176 K cells to 627 K cells. It was found that the heat transfer coefficient values from Mesh 3 and Mesh 4 are consistent; thus, Mesh 3 was used in the remainder of this work to reduce computational cost. The mesh is shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mesh** & **Number of Cells** & **Heat Transfer Coefficient (W/m\({}^{2}\).K)** \\ \hline Mesh 1 & 176,409 & 49.12 \\ Mesh 2 & 319,311 & 47.53 \\ Mesh 3 & 383,599 & 47.47 \\ Mesh 4 & 627,830 & 47.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mesh sensitivity analysis at wind speed of 7 m/s and solar irradiance of 800 W/m\({}^{2}\).  
Figure 2: CFD domain.