esa-sceva/satcom-chunk-collection huggingface dataset:
# Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer's Disease
## 3 Results
### Drugs Interact with Differentially Expressed Genes in AD  
We submitted the list of 76 DEGs to DGIdb, DrugCentral, and The Drug Repurposing Hub. We selected the drugs that interact with our DEGs in at least two of the drug databases.  
Figure 2: Venn diagram analysis of differentially expressed genes: (**A**) down-regulated genes; (**B**) up-regulated genes.  
DGIdb and DrugCentral revealed several drugs that have as targets two down-regulated genes: GOT1 and MET1. GOT1 is a target of aspartic acid and MET is a target of crizotinib, erlotinib, cabozantinib, gefitinib, pazopanib, and afatinib. In addition, the results from DGIdb and DrugCentral show that two up-regulated genes, COMT and PTAFR, act on common drugs in the two drug databases. Opicapone, tolcapone, and entacapone interact with COMT, and rupatadine interacts with PTAFR.  
DGIdb and The Drug Repurposing Hub showed nine down-regulated genes (CA10, CACNG2, CALY, CKMT1A, GAD1, GLS2, GOT1, MAP4, and MET) interacting with known drugs. CA10 interacts with zonisamide, CACNG2 with gabapentin, CALY with apomorfina, clozapina, and trifluorerazine, CKMT1A with creatin, GAD1 and GLS2 with glutamic acid, GOT1 with aspartic acid, cysteine, and glutamic acid, and MAP4 with docetaxel and paclitaxel. MET is a target of alectinib, alitriatinib, AMG-208, AMG-337, amuvatinib, BMS-777607, BMS-817378, cabozantinib, crizotinib, foretinib, golvatinib, JNJ-38877605, MGCD-265, MK-2461, MK-8033, PF-04217903, PHA-665752, savolitinib, andtivantinib. In addition, three up-regulated genes (COMT, PRKG1, and PTAFR) are targets of different drugs. COMT interacts with entacapone, nialamide, opicapone, tolcapone, and 2-metossiestradiol, PRKG1 is a target of GSK-690693, and PTAFR is a target of apafant, foropafant, and rupatadina.  
DrugCentral and The Drug Repurposing Hub revealed several drugs acting on two down-regulated genes (GOT1 and MET). GOT1 is a target of aspartic acid; MET is a target of cabozantinib, and crizotinib. In addition, we found two up-regulated genes: COMT interacts with entacapone, opicapone, and tolcapone; PTAFR interacts with rupatadine.  
Tables 1 and 2 show the list of down-regulated and up-regulated genes that are targets of known drugs in at least two drug databases.  
Overall, we obtained 34 drugs that interact with nine genes for down-regulated genes, and nine drugs that interact with three genes for up-regulated genes.
# A Wireless EEG System for Neurofeedback Training
## 6 Limitations  
Due to the variable characteristics of the EEG signal, the development of a battery-powered mobile Wi-Fi NF-EEG system is challenging. Some professional EEG-NF devices are available from many vendor companies, but due to the high price, they are not intended for public use. Scaling and reconfiguring the hardware to allow for over 64 channels would reduce many of the technical characteristics of mobile systems. The only limitation of a stand-alone system with a high-performance processor operating at maximum load is the maximum battery life. Charging it complicates the collection of EEG data in these devices. Participant data security may be at risk on account of the sensitivity of wireless connections or due to any movement of cables. Therefore, wireless EEG systems could encrypt data prior to wireless transfer and use shorter sensor-ADC connections or a head mounting. Overall, the price of the system only increases due to the headset adjustability, requiring multiple headset caps for people with different head sizes.
# High-Energy Neutrino Flavor State Transition Probabilities
## 3 Oscillation and the Mean Free Path
In other words, if we determine a state in \(A\), that does not mean we know for certain state \(B\), rather that all other probabilities for state \(B\) go to zero. To consider the probability of an arbitrary neutrino state prediction, we must first consider a known constraint. To make a prediction based on a flavor eigenstate, we must assume that all mass eigenstate probabilities collapse. Conversely, if we are to make a prediction based on a mass eigenstate, all the flavor eigenstate probabilities collapse. However, we must consider our neutrino eigenstates: \(\ket{v_{e},v_{\mu},v_{\tau}}\) and \(\ket{m_{1},m_{2},m_{3}}\). Because we are aware of our constraints, the same assumption made previously strictly applies here. That is, the outcome observed for \(A\) does not depend on the outcome observed for \(B\) and the outcome observed for \(B\) does not depend on the outcome observed for \(A\). We can now determine the probability for flavor transition from \(v_{e}\to v_{\mu}\), which corresponds to the detection of \(v_{e}\) and is dependent on \(\Delta m\) as the mass difference, where \(\Delta m_{ij}^{2}=m_{i}^{2}-m_{j}^{2}\), with \(E\) as the neutrino energy and the distance from the source, \(L\). \[P(v_{e}\to v_{\mu})=1-sin^{2}(2\theta_{21})sin^{2}(\frac{1.27\Delta m_{21}^{2} [eV]L[m]}{E[MeV]}) \tag{14}\]Alternatively, we can show the probability of our mass eigenstate and the prediction of the flavor state remaining in \(\
u_{e}\). We will reserve component \(\lambda\) as an arbitrary flavor state. The constraint here, however, is that certainty in one eigenstate negates certainty in the other [2]. For comparative purposes, on Earth, we can expect a solar neutrino flux on the order of \(\sim\)\(5.9\times 10^{14}\)m\({}^{-2}\) with approximately \(1.7\times 10^{38}\) neutrinos being emitted every second. Consider the Type II supernova, SN1987A, located in the Large Magellanic Cloud, which was first detected on 23 February 1987. During its luminosity peak, approximately \(5.9\times 10^{19}\) neutrinos were detected on Earth per second, with an average flux of approximately \(1.3\times 10^{14}\)m\({}^{-2}\). The distance \(d\) from SN1987A to Earth is \(\sim\)\(168,000\) light years [11].
# Improved Calculation Method for Siphon Drainage with Extended Horizontal Sections
## 3 Analysis of the Formation Mechanism of Wet and Dry Slug Flow
### Improved Calculation Method for Siphon Flow Rate with Long Horizontal Pipe Sections
When \(Ca>10^{-3}\), it can be judged as a wet slug flow, and the mathematical models given in Equations (26)-(32) were used to calculate the velocity of each gas-liquid phase, the volume fraction of the gas, and the length of the bubble slug when the bubble size is known. The calculated flow velocity was used as the liquid velocity, i.e., \(U_{LS}=v_{hori}\), and the equation proposed by [PERSON] [18] was used to determine the initial approximation of the bubble velocity  
\[U_{B}^{0}\approx 2.27U_{LS}+0.361(gd)^{0.5} \tag{53}\]  
Figure 8: Flowchart of velocity calculation of the siphon with a long horizontal section. Then, the capillary number was calculated by using the bubble velocity, and the liquid film thickness \(\delta\) was determined using \(\frac{\delta_{0}}{R}=\frac{1.34C\frac{a^{\frac{3}{2}}}{1+1.34\times 2.5 Ca^{\frac{3}{ 2}}}}{1+1.34\times 2.5 Ca^{\frac{3}{2}}}\)[38]. The pressure gradient could be calculated after n iterations of Equations (31) and (32), while the gas flow rate in the bubble, the liquid flow rate in the liquid film, and the bubble motion velocity could be obtained after n iterations of Equations (26) and (27). The relative error \(\psi=\frac{2(|q_{LS}-q_{B}|-|q_{lf}|)}{(|q_{LS}-q_{B}|+|q_{lf}|)}\) can be used to compare the liquid flow rate in the film and the calculated error \(\varpi\) specified value can be used to decide whether to terminate the calculation (when \(\psi\leq\varpi\)) or return to re-substitute the calculated capillary number and repeat the step (when \(\psi>\varpi\)). When \(Ca<0.001\), it can be judged as dry slug flow. Take the calculated flow velocity as the gas-liquid two-phase velocity \(U_{LS}=v_{hori}\), and calculate the moving friction loss of the gas-liquid-solid three-phase contact line according to Equation (41). The value of the slug frequency in the tube at the corresponding flow rate was obtained by Equation (49). In addition, the air pressure, gas precipitation, gas motion velocity, and liquid motion velocity in the tube after n iterations were determined using Equation (52). Comparing the values of the selected parameters, i.e., \(\phi=\frac{|U_{LS}-U_{B}|}{|U_{LS}+U_{B}|}\), the specified value of the calculation error \(\Delta\) determines whether to terminate the calculation (when \(\phi\leq\Delta\)) or to return and repeat the previous ones.
## 3 Results  
### Obstacle Avoidance Performance  
#### 3.1.1 Obstacle Detection and Response  
The obstacle avoidance system was designed to prevent collisions by integrating the Multi-Ranger Deck, which utilizes time-of-flight (ToF) sensors to detect objects in the drone's surroundings. Upon detecting an obstacle within \(\rho_{th}=0.35\) m, the drone autonomously retreats \(\rho_{avoid}=0.2\) m to ensure safe clearance. These values were selected to ensure the drone reacts promptly without excessive movement, striking a balance between responsiveness and stability.  
The Multi-Ranger Deck can measure distance up to 4 m within a few millimeters, as provided by Bitcraze Bitcraze (2018). This level of accuracy allowed for consistent and reliable obstacle detection throughout testing, as proved by Table 1. Also, the avoidance parameters were implemented as adjustable variables in the code, enabling customization based on the user's specific requirements or environmental constraints.  
To evaluate the obstacle avoidance system's reliability, structured tests were conducted where the drone was flown perpendicularly toward an obstacle placed at various orientations. For each direction--front, back, left, right, and angled (with the obstacle positioned diagonally)--the drone was commanded via gesture control to fly directly toward the object. Each scenario was tested 25 times, for a total of 125 trials. As shown in Table 1, the drone successfully avoided collisions in all cases, achieving a 100% avoidance rate across all directions.  
During testing, when the drone's battery was fully charged and all systems were functioning optimally, there were no instances where the drone failed to react to an obstacle and flew directly into it. The system reliably detected and responded to obstacles, enhancing the drone's ability to navigate safely in constrained environments.
## 2 OTFS Modulation
### Receiver Processing  
The received signal is obtained by computing the Heisenberg transform and adding additive white Gaussian noise (AWGN) with monolateral power spectral density (PSD) \(N_{0}\), referred to as \(n(t)\). Thus, the received signal is  
\[r(t)=\iint h(\tau,\
u)s(t-\tau)e^{j2\pi\
u(t-\tau)}d\tau d\
u+n(t), \tag{5}\]  
At the receiver side, inverse operations are performed to recover the received symbols in the DD domain. Firstly, the receiver computes the Wigner transform by sampling the so-called cross-ambiguity function (CAF) \(A_{g_{rx},r}(f,t)\) to convert the received time-domain signal into samples in the time-frequency grid as  
\[Y_{tf}[l,k]=A_{g_{rx},r}(f,t)|_{f=l\Delta f,f=kT}=\left.\int r(t^{\prime})g_{ rx}^{*}(t^{\prime}-t)e^{-j2\pi f(t^{\prime}-t)}dt^{\prime}\right|_{f=l\Delta f,f=kT}. \tag{6}\]  
Secondly, a SFFT is applied to obtain the DD-domain received frame  
\[Y[m,n]=\left.\frac{1}{\sqrt{MN}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}Y_{tf}[l,k]e^{ -j2\pi\left(\frac{n}{N}-\frac{n}{N}\right)}.\right. \tag{7}\]  
From now on, we are going to consider OTFS modulation with rectangular shaping filters of duration \(T\) and amplitude \(1/\sqrt{T}\) at both the transmitter and receiver side.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 2 Design of Cascaded Adaptive Vector Tracking Loop  
The vector tracking loop can share information between channels. The measurement noise is distributed in the form of weights to estimate accurately the loop phase error and improve the dynamic performance and accuracy of the GNSS receiver. In order to achieve GNSS signal acquisition, tracking and positioning solution, the vector tracking loop structure is mainly composed of a code/carrier discriminator, a KF preprocessor, and an EKF navigation solution. The collected intermediate frequency (IF) signals are processed by local signal correlation for coherent integration. The discriminator output is used as the input of the KF. The output information is the code phase error and the carrier phase error. Through a linear transformation, it is converted into a pseudo-range error and a pseudo-range rate error, which are used as the measurement information of the navigation filter processor. The receiver's PVT and other navigation information are obtained through filter estimation, and the local code/carrier NCO is updated with the calculated pseudo-range and pseudo-range rate to generate a local replica signal. The block diagram is shown in Figure 1.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 3 Specific Measurement Methods
### Optical Particle Characterization
#### 3.1.1 Particle Image Velocimetry
In cases with high particle concentrations or large imaging areas, there are probably several particles in each interrogation area, making conventional digital cross-correlation PIV quite practical. If particle concentration is so low that each interrogation area only includes one particle, then PTV can be used to explore the particle trajectory and velocity using a Lagrangian approach. It is difficult to measure the velocity of the carrier phase using PIV, where there is interruption of the dispersed phase, as is the case for LDA. Examples of simultaneous measurement of two-phase flow using PIV are described in detail in [PERSON] [19] and [PERSON] and Longmire [20]. Usually, it is possible to distinguish the dispersed-phase particles from tracer particles using obvious differences in size and luminosity, especially when the tracer particle is small enough [19; 21]. However, some issues exist; for example, when tracer particles are in shadows behind extremely large particles, the camera does not capture them. Based on a technique proposed by [PERSON] [21], [PERSON] and [PERSON] [22] used a low signal threshold to eliminate noise from the images and an object-growing algorithm to reduce halo effects around particles; thus, larger particles were more easily identified. Measuring the carrier-phase velocity very close to the particle surface is still one of the most difficult issues in experimental studies. This is because tracer particle images near the particle surfaces are usually eliminated as image noise. [PERSON] and [PERSON] [23] adopted a very-high resolution PIV system, with an image area of only 3.7 mm \(\times\) 4.7 mm to mitigate this problem. Theoretically, phase discrimination based only on particle image size causes errors related to zero crosstalk. Despite this improvement, the measurement area was so small that hundreds of particle image pairs were required to achieve adequate turbulence statistics. In short, high-spatial resolution PIV techniques are the most effective experimental method for particle-laden flows with low volumetric concentrations, while high-time resolution PIV and holographic PIV techniques show great potential for turbulence analysis and three-dimensional pressure field reconstructions. However, ongoing technological progress is required, before robust testing technologies are developed [24]. In reality, micro-PIV still fails to measure microscale gas flows accurately; thus, it has a long way to go before reliable measurements can be acquired for microparticle-laden gas flows.
###### Abstract  
Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN's forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics.  
induced ordered weighted averaging operator; machine learning; sparrow search algorithm; residual correction; agricultural product price forecast
# Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection
## 5 Discussion  
As noted above, the modification adds to the model and turns it into a larger version of DenseNet. The upgrade seems to be small but it led to notable consequences. We present a few discussions and experiments to prove the efficiency of our proposed method.  
Feature reuse: Generally, the feature maps close to the input detect small or fine-grained detail, whereas feature maps close to the output of the model capture more general features. The connection between blocks encourages layers to learn both small detail and general nature of the object. The ablation study also proves the effect of skip connections that is more connections results in better performance.  
Adaptability: The method of adding long skip connections has the ability to fit well to other models, from lightweight one like MobileNet family to well-designed EfficientNet family. Comparative experiments show that the modified models converge faster and have better performance.  
Difficulty: In order to perform long skip connection between layers having different size, we have to equal the shape of two layers (detail in Section 3.6). This task can consume large GPU memory capacity and is unable to work with small size GPU, as the shape of feature map of the long skip connection layer is very large.
## 1 Introduction
The WPG and SEG systems are characterized by unidirectional power flow, which are unlike the grid-connected ESSs that are characterized by bidirectional power flow. Therefore, the LVRT control strategy used for the grid-connected ESSs needs to comply with the LVRT requirement, additionally, the characteristics of bidirectional power flow, i.e., the charging conditions of the grid-connected ESSs, need to be considered in the LVRT control strategy. The LVRT control strategy for the ESS is presented in [34]. In [34], when the grid voltage drops under charging condition of the grid-connected ESS, the LVRT control strategy is applied after the condition of the grid-connected ESS is changed to the discharging condition. It contributes to increase the voltage at the point of common coupling (PCC). This LVRT control strategy can be applied in applications improving the grid voltage durability such as frequency regulator that improves the grid frequency stability [35], [36]. However, in applications with other system connected to the DC-link of the grid-connected ESS, the charging condition of the grid-connected ESS must be maintained although the grid voltage drops. This paper presents the  
Figure 1: Low voltage ride through (LVRT) requirements in Germany and China. LVRT control strategy for grid-connected ESSs by considering the characteristic of bidirectional power flow. The proposed LVRT control strategy complies with the LVRT requirement and operates for contributing to increase voltage at the PCC. In other words, when the grid voltage drops, the proposed LVRT control strategy determines the injection quantity of the active and reactive currents depending on the voltage drop ratio of the three-phase grid. Additionally, in this paper, we analyzed the variations of the PCC voltage depending on the phase of the reactive current of the grid-connected ESS during the charging and discharging conditions. The validity of the proposed LVRT control strategy is verified and the variations of the PCC voltage of the grid-connected ESS are analyzed by PSIM simulation and experimental results.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
###### Abstract  
Multicore architecture is applied to contemporary avionics systems to deal with complex tasks. However, multicore architectures can cause interference by contention because the cores share hardware resources. This interference reduces the predictable execution time of safety-critical systems, such as avionics systems. To reduce this interference, methods of separating hardware resources or limiting capacity by core have been proposed. Existing studies have modified kernels to control hardware resources. Additionally, an execution model has been proposed that can reduce interference by adjusting the execution order of tasks without software modification. Avionics systems require several rigorous software verification procedures. Therefore, modifying existing software can be costly and time-consuming. In this work, we propose a method to apply execution models proposed in existing studies without modifying commercial real-time operating systems. We implemented the time-division multiple access (TDMA) and acquisition execution restitution (AER) execution models with pseudo-partition and message queuing on VxWorks 653. Moreover, we propose a multi-TDMA model considering the characteristics of the target hardware. For the interference analysis, we measured the L1 and L2 cache misses and the number of main memory requests. We demonstrated that the interference caused by memory sharing was reduced by at least 60% in the execution model. In particular, multi-TDMA doubled utilization compared to TDMA and also reduced the execution time by 20% compared to the AER model.  
avionics systems; real-time system; execution model; multicore architecture; interference analysis Article