esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) has high sensitivity in detecting volatile compounds, making it commonly used for analyzing volatile compounds in food [17; 18], especially in milk and dairy products [19; 20]. Previous studies have confirmed that the differences in the types and contents of nutrients in yak milk powder can affect the variety and concentration of volatile compounds [8]. For example, certain fatty acids and esters, which contribute to the desirable fruity or creamy flavors in milk, are closely associated with higher fat content. Conversely, some undesirable volatile compounds, such as aldehydes, may be formed as breakdown products of lipid oxidation or protein degradation. Moreover,the correlation between nutrient content and volatile compounds in yak milk may vary depending on the lactation period, with fat content typically being the most significant factor in determining the overall volatile profile. However, there has been limited research on the volatile compound profiles of yak milk powder from different lactation periods. Research on goat milk has shown that volatile fatty acids change during lactation, directly influencing the milk's flavor characteristics [21]. Similarly, studies on donkey milk have revealed lactation-related variations in amino acids, which are important components contributing to the milk's characteristic odor and flavor [22]. These findings suggest that the volatile compound profiles in dairy products can serve as indicators of lactation stages. Thus, we hypothesized that volatile compounds in yak milk powder made from milk collected during different lactation periods might exhibit certain differences, potentially allowing for the identification and classification of yak milk's collection period by analyzing the volatile compounds. In this study, we determined the volatile compounds in freeze-dried colostrum powder (YCSP), freeze-dried mature milk powder (YMMP), and freeze-dried ending milk powder (YEMP) using HS-SPME-GC-MS. Furthermore, we effectively distinguished the flavor differences among yak milk powder samples harvested during different lactation periods through Principal Component Analysis, OPLS-DA model analysis, and hierarchical clustering analysis. The research results provide valuable insights for a deeper evaluation of yak milk powder flavor and the identification of yak milk harvesting periods, which can benefit the dairy industry by enhancing product differentiation and quality control. Additionally, the methodologies used in this study can be replicated in other dairy product research to explore similar flavor profiles and volatile compound differences across different milk types and stages.
## 6 Discussion
### Effect of Preload Distribution on Fatigue Resistance Performance  
In order to explore the influence of preload distribution on the fatigue resistance performance of three bolted joints with double nuts, the preload distributions assigned to the upper nut were changed. After the tightening simulations were completed, one-cycle vibration simulation under the same conditions was performed, and the von Mises stresses of root points were extracted. The obtained stress time series and stress range histograms of the three thread connection structures are shown in Figures 30-32, respectively. From these figures, it can be found that the von Mises stresses of root points were larger when the preloads assigned to the upper nuts were smaller (5 kN). The minimum stresses at root points were almost the same for different preload distributions on the upper and lower nuts. From the figures of stress range histograms, it can be observed that the stress cycles extracted via the rainflow counting method are mostly low-amplitude, while only a few high-amplitude stress cycles are present.  
Figure 30: Stress time series and stress range histograms of bolted joints installed with regular double nuts: (**a**) stress time series at the root point;(**b**) stress range histogram.  
Figure 29: Curves of preload decrease for bolted joints with an eccentric double nut under different preload distributions.  
The fatigue damage indexes accumulated within one vibration cycle were calculated, as listed in Table 8. The damage indexes change regularly as the preload assigned to the upper nut is increased. The larger the preload assigned to the upper nut, the smaller the damage index, indicating better fatigue resistance performance. Therefore, it is recommended to increase the proportion of preload assigned to the upper nut in engineering applications with the purpose of extending the fatigue life of thread connection structures.
# Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel
## 4 Proposed Model
### Detection  
We split the signal \(z[j,k]\) into two branches; one is processed along the horizontal direction to extract the LRI, whereas the other is processed along the vertical direction to extract the ULI. Each branch includes an interference estimator, which removes the estimated interference and detects 1D interference. The interference estimator is presented in Section 3. The output of the interference estimator (i.e., LRI or ULI) is removed from the signal \(z[j,k]\) to create signals \(\overline{r}_{h}[j,k]\) and \(\overline{r}_{v}[j,k]\), which are affected by the 1D interference (i.e., VI or HI). The estimated interference is removed according to the estimated signal \(a[j,k]\). We consider Equation (31) as follows:  
\[\begin{array}{l}LRI[j,k]=v[j,k+1]+v[j,k-1]\\ =a[j+1,k+1]t+a[j+1,k-1]t\\ +a[j,k+1]r\qquad+a[j,k-1]r\\ +a[j-1,k+1]t+a[j-1,k-1]t\end{array} \tag{41}\]  
From Equation (41), we can use signal \(a[j,k]\) to estimate the LRI. Therefore, we can apply VA to signal \(\overline{r}_{h}[j,k]\) to achieve signal \(\overline{s}_{h}[j,k]\), which is the estimated signal and is similar to signal \(a[j,k]\). Thereafter, the signal \(\overline{s}_{h}[j,k]\) goes through the VI target to achieve the estimated signal of the LRI (i.e., \(\overline{LRI}[j,k]\)) with higher accuracy. Similarly, we can improve the ULI and achieve signal \(\overline{ULI}[j,k]\). We remove interferences (\(\overline{LRI}[j,k]\) and \(\overline{ULI}[j,k]\)) from the signal \(z[j,k]\) and apply SOVA to detect this signal. Finally, we use the average function to combine the output signals from the horizontal and vertical SOVAs. \(\hat{u}[k]\) is obtained from the demodulated \(\hat{u}[j,k]\).
## Case Study: Product Development
### Prototyping  
The realized prototype of the system consisting of the designed components is illustrated in Figure 6, where the core of the system is the Arduino-compatible WiDo WiFi WG1300 module equipped with a microcontroller ATmega32u4 and supporting communication with the 2.4-GHz IEEE 802.11 b/g standard. Moreover, the system consists of the Inertial Measurement Unit (IMU), AltIMU-10 v4 with built-in three-axis gyroscope, accelerometer and magnetometer and altimeter. Both devices are mounted on the Polar's T34 Heart Rate Transmitter chest strap and powered with the Li-Pol Redox 1800 mAh 20C 2S 7.4-V battery.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ToD** & **SoA** & **ZoA** & **FoA** & **DoA (min)** & **CoB *** \\ \hline Morning & Bathroom & of lying & Lying & 10/15/30/120 & Normal \\ Afternoon & Bedroom & of sitting & Standing & 15/23/45/180 & Suspicious \\ Evening & Antechamber & of standing & Sitting & \textgreater{}15/\textgreater{}23/\textgreater{}45/\textgreater{}180 & Danger \\ Night & Kitchen & of walking & Walking & - & - \\ - & Living room & - & Tumble & - & - \\ \hline \hline \end{tabular}  
* ToD, Time of Day; SoA, Section of Apartment; ZoA, Zone of Activity; FoA, Form of Activity; DoA, Duration of Activity; CoB, Class of Behavior.  
\end{table}
Table 2: Components of the behavior vector.  
Figure 5: Behavior classification scheme.  
The behavior identification and its classification are implemented on a Lenovo ThinkPad T440s with i5-4200u 1.6-GHz CPU and 8 GB of RAM with the Windows 7 64-bit operating system and modeling environment MATLAB Version 2015a.
###### Abstract  
Introduction: Thermal damage to the kidney during holmium laser lithotripsy is a serious complication, which cannot always be prevented considering the diversity of conditions in the kidney and the lack of technical capability to measure intrarenal temperature in real-time. The aim of this study was to analyze the impact of different power settings of the holmium laser and the flow rate of irrigation fluid conditioned by the size of the sheath on the fluid temperature in a model of the pelvicalyceal system during lithotripsy. Materials and Methods: A lithotripsy of artificial stones was performed in a 3D-printed model of the renal pelvicalyceal system with a volume of 20.5 mL. A gravity-driven fluid flow was applied (\(p\) = 60 cm H\({}_{2}\)O). A 9.2 Fr ureteroscope was introduced into the model through a ureteral access sheath of either 10/12 Fr or 12/14 Fr. Lithotripsy power ranged from 12 to 25 W. Temperature was measured using thermography. Results: For the 10/12 Fr ureteral access sheath, the fluid flow rate was 2.7 mL/min, with the maximum temperature reached within 20 min ranging from 45 to 50 \({}^{\circ}\)C. For the 12/14 Fr ureteral access sheath, the fluid flow rate was 9.8 mL/min, with the achieved temperature ranging from 35 to 45 \({}^{\circ}\)C. Laser power influenced the rate of temperature increase and the maximum temperature reached. During the first 5 min, the rate of temperature increase was solely dependent on laser power, not on the flow rate. Temperatures of 40 \({}^{\circ}\)C were only not exceeded with high flow rates (12/14 Fr sheath) and power below 20 W. Conclusions: In a short period, the rate of temperature increase depends solely on laser power, not on the irrigation flow rate. During prolonged lithotripsy, the maximum temperature value depends on both parameters.  
retrograde intracranial surgery; intrapelvic temperature; 3D printing; ureteral access sheath 2024143501[[https://doi.org/10.3390/app14083501](https://doi.org/10.3390/app14083501)]([https://doi.org/10.3390/app14083501](https://doi.org/10.3390/app14083501))
## 4 Extension of Traditional Impedance Tracking Topology  
### Overall Impedance Matching Topology  
In this system, the efficiency tracking point is smaller than the equivalent resistance point, implying that RL is greater than Re. Therefore, the buck circuit's active impedance matching form was adopted in this system. The overall impedance matching topology is shown in Figure 6. In this figure, Vd represents the AC voltage source; S1, S2, S3, and S4 are the four switch transistors; and Lp and Ls and Cp and Cs are the resonant inductors and capacitors on the primary and secondary sides, forming the resonant network of the entire system. Rp and Rs are the internal resistances of the primary and secondary coils; D1, D2, D3, and D4 are the unidirectional diodes; Cr is the inter-stage capacitor; Sb represents the buck circuit control switch transistor; Db is the unidirectional diode; Lb is the buck circuit inductor; Cb is the buck circuit capacitor; and Rb represents the system's equivalent resistance.  
The overall impedance matching topology, as shown in Figure 6, consists of an inverter circuit, resonant circuit, rectifier circuit, and buck circuit, without additional circuits. The inverter circuit is formed by four switch transistors. The resonant circuit consists of primary and secondary side resonant inductors and capacitors, as well as the internal resistances of the primary and secondary coils. The rectifier circuit consists of four unidirectional diodes. The buck circuit comprises switch transistors, diodes, an inductor, a capacitor, and output resistance.  
The core control of this topology lies in the conduction and switching control of the buck circuit's switch transistor Sb, which controls the buck circuit's duty cycle D and, consequently, the continuity or discontinuity of the current flowing through the inductor Lb. This system enables the tracking and detection of the resistance value of the output resistance Rb in both the continuous conduction mode (CCM) and discontinuous conduction mode  
Figure 5: Impedance matching boost circuit.  
Figure 6: Overall impedance matching topology.  
(DCM), expanding the range of impedance variations. We also analyzed the switching criteria between these two modes and the operation of CCM and DCM modes.
# Weight Adjustment Framework for Self-Attention Sequential Recommendation
## 3 Methodology
### Filter Layer  
The filter layer is a denoising module. User-interaction data recorded on online platforms inevitably contain noise. Previous studies, such as SSE-PT [25], TiSASRec [27], and AC-TSR [15], although demonstrating significant recommendation effectiveness in sequential recommendation systems, did not systematically consider the handling of input data noise in their model designs. This oversight may limit their robustness in practical application scenarios. To solve the problem of the inaccurate allocation of attention weights due to noise input, we added a filter layer to WAF-SR. The filter layer serves to identify and remove noise or irrelevant interaction data from the input user-interaction sequences. This layer utilizes signal processing techniques to preprocess the input sequences, ensuring that the data passed to the self-attention layer are purified. Specifically, the embedding matrix of the item \(e^{u}\) is first transformed into the frequency domain via the fast Fourier transform (FFT) [28; 29]:  
\[x^{l}=\mathcal{F}(e^{u})\in\mathbb{C}^{n\times d} \tag{2}\]  
where \(\mathcal{F}(\cdot)\) denotes the one-dimensional fast Fourier transform, \(x^{l}\) is a complex tensor representing the spectrum of \(e^{u}\), \(\mathbb{C}\) represents the complex field, and \(\mathbb{C}^{n\times d}\) represents a complex matrix with \(n\) rows and \(d\) columns. The spectrum is modulated by multiplying it with a learnable filter \(W\in\mathbb{C}^{n\times d}\):  
\[\overset{\sim}{x^{l}}=W\bigodot x^{l} \tag{3}\]  
where \(\bigodot\) represents element-wise multiplication. Then, modulate the spectrum \(\overset{\sim}{x^{l}}\) back into the time domain through the inverse fast Fourier transform to update the sequence representation:  
\[Z^{l}=\text{LayerNorm}\left(e^{u}+\text{Dropout}\left(\mathcal{F}^{-1}{ \overset{\sim}{x^{l}}}\right)\right) \tag{4}\]  
where \(\mathcal{F}^{-1}(\cdot)\) stands for the one-dimensional inverse fast Fourier transform, which converts the complex tensor into a real-valued tensor. To prevent gradient vanishing, skip connections [30], layer normalization [31], and dropout [32] operations are also introducedin the filter layer. It can be summarized that the item's embedding matrix \(e^{u}\) passes through the filter layer to obtain the filtered matrix \(Z^{l}\):  
\[Z^{l}=FL(e^{u}) \tag{5}\]  
where \(l\in{0,1,2,\ldots,n}\) represents the number of layers of the filter layer, \(FL\) denotes the filter layer. When \(l=0\), \(Z^{l}=e^{u}\).
# Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus
###### Abstract  
In this work, using a Richards-Wolf formalism, we derive explicit analytical relationships to describe vectors of the major and minor axes of polarization ellipses centered in the focal plane when focusing a cylindrical vector beam of integer order n. In these beams, the major axis of a polarization ellipse is found to lie in the focal plane, with the minor axis being perpendicular to the focal plane. This means that the polarization ellipse is perpendicular to the focal plane, with its polarization vector rotating either clockwise or anticlockwise and forming \"photonic wheels\". Considering that the wave vector is also perpendicular to the focal plane, we conclude that the polarization ellipse and the wave vector are in the same plane, so that at some point these can coincide, which is uncharacteristic of transverse electromagnetic oscillations. In a cylindrical vector beam, the spin angular momentum vector lies in the focal plane, so when making a circle centered on the optical axis, at some sections, the handedness of the spin vector and circular motion are the same, being opposite elsewhere. This effect may be called an azimuthal transverse spin Hall effect, unlike the familiar longitudinal spin Hall effect found at the sharp focus. The longitudinal spin Hall effect occurs when opposite-sign longitudinal projections of the spin angular momentum vector are spatially separated in the focal plane. In this work, we show that for the latter, there are always an even number of spatially separated regions and that, when making an axis-centered circle, the major-axis vector of polarization ellipse forms a two-sided twisted surface with an even number of twists.  
Richards-Wolf formalism; sharp focus; polarization ellipses; cylindrical vector beam; spin angular momentum; photonic wheels; transverse spin Hall effect; polarization stripe; twisted surface +
Footnote †: journal: Physics Letters B
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 4 Double Deep Q-Network with Dual-Agent Algorithm
### Neural Network Structure  
Inspired by networks of DQN, we propose a network structure to train the agent \(P\) and agent \(A\). The whole network structure for two agents is shown in Figure 6. To obtain an effective traffic signal control policy, convolutional neural networks (CNNs) are used to extract critical information from vehicle position matrix. Parameters of convolutional networks are shared between two agents since the traffic environment is public.  
Figure 5: The intersection state defined as a matrix. All states are extracted from incoming roads.  
The state is fed into the first convolutional layer with 32 filters of 4 \(\times\) 4 with the stride 2 and the second convolutional layers with 64 filters of 2 \(\times\) 2 with stride 1, where both of them leverage the Rectified Linear Unit (ReLU) as the activation function. Finally, the fully-connected networks (belonging to busy agent) with 64 (ReLU) and 32 hidden units linearly generate Q-values whose dimensions represent agent actions.  
To prevent the overestimation of the Q-value, the DDQN algorithm is adopted. It defines a new target as  
\[Q_{target}=r+\gamma Q(s^{\prime},\arg\max_{a^{\prime}}Q(s^{\prime},a^{\prime}; \theta_{i})) \tag{10}\]  
where \(\theta_{i}\) is the evaluating network parameters at the iteration \(i\). In (10), the maximized Q-value of the DQN target is replaced by an action \(a^{\prime}\) based on the maximal Q-value of evaluating networks to indirectly reflect the best Q-value. Therefore, the overestimation of the Q-value is eliminated.
## 3 Results and Discussion
### Plate Mechanical Stability  
The mechanical stability and stress distribution in Figure 6 shows a clear increasing trend with an increase in the volume reduction of bone plates. The stresses value increased up to 13% with a volume reduction of 75%, considering both screw configurations. This validates the topology optimization (SIMP) algorithm; by maximizing the design's stiffness and controlling its volume reduction, the bone plates are now capable of distributing increased stresses at the fracture region. This is a clear indicator that the stress shielding phenomenon is reducing by optimizing the plates. All plates resulted in less stresses than the tensile strength of the Ti-4 Al-6V and presented an acceptable safety factor (SF), which is always intended to be higher than >1 for any design to be accepted [49]. However, further biomechanical parameters such as screw threads must be considered in the finite element model to represent their role on implant-screw load transfer.
# Deep Learning-Based Image Recognition of Agricultural Pests
###### Abstract
However, these deep-learning models usually perform resize operations on samples, leading to the loss of sample features for pest samples of considerable size, and the excellent performance of deep-learning models is determined mainly by the number of examples in the dataset. Therefore, pest datasets with a small number of samples and too-large image sizes are undoubtedly a great challenge for target-detection models aiming to perform well. This paper proposes a model for detecting farmland pests in response to the problems of large and unbalanced sample categories, large image sizes, and a small number of samples in farmland pest datasets. Specifically, this study uses a sliding window cropping method to learn the sample features inside larger images carefully. Secondly, this paper introduces an attention mechanism inside the model to focus more on learning the sample features that are more important for the task at hand. Finally, data augmentation is performed on a smaller number of samples in the category. In this way, the model could learn sample features in a more comprehensive and detailed way to improve the efficiency of pest detection. The main contributions of this paper are as follows. 1. We propose a new pest-identification model that aims to achieve good performance, even with a small number of samples, unbalanced categories, and large sample image sizes;
2. We devise a new sliding window cropping method that aims to increase the perceptual field to learn sample features more carefully and comprehensively, which may be missed due to large image sizes;
3. We perfectly integrate the attention mechanism with the FPN layer in the model to make the model more focused on sample features that are more useful for the task at hand;
4. We augment the data for small numbers of sample categories as well as for unbalanced samples to prevent their adverse effects.
## 3 Test Results
### Clogging Zone  
The variation in clogging zone width of the slurry over time is shown in Figure 12. During the first 24 h of the vacuum preloading test, the clogging zones of all four slurry types developed rapidly. The lime-flocculated slurry exhibited the fastest development rate, with the clogging zone reaching 165 mm and stabilizing at 24 h. The development rate of the clogging zone gradually slowed between 24 and 100 h. The clogging zones of the APAM-flocculated slurry and the dual use of PAC and APAM-flocculated slurry stabilized at 68 h and 92 h, respectively, with final widths of 46 mm and 71 mm. After 100 h, only the untreated slurry continued to exhibit a slow increase in clogging zone width, eventually stabilizing at 41 mm.  
These results indicate that flocculation treatment not only accelerates the development of the clogging zone but also increases its final width. The fitting curves for the clogging zone widths of the lime-flocculated slurry, the dual use of PAC and APAM-flocculated slurry, the APAM-flocculated slurry, and the untreated slurry are as follows:  
\[r=163.42-141.52\mathrm{e}^{-t/4.9} \tag{1}\]  
Figure 12: Clogging zone width of slurry versus time.  
\[r =79.4-70.64\mathrm{e}^{-t/13.44} \tag{2}\] \[r =138.14-35.8\mathrm{e}^{-t/19.2}\] (3) \[r =70.63-65.56\mathrm{e}^{-t/21.41} \tag{4}\]