esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 4 Results
### Accuracy Assessment Results
Among them, the biases of MLP (Figure 5a) and GBDT (Figure 5d) are larger and the degree of underestimation of the GPP is higher. In Figure 5c,f, the number of points distributed on both sides of the reference line is approximately equal. This shows that the prediction results of Adaboost and LightGBM have no obvious tendency to overestimate or underestimate. Among them, the points in Adaboost (Figure 5d) are relatively scattered and the display error is large; the points in LightGBM (Figure 5f) are very close to the reference line and the display error is small. Figure 5: The fitting results of model prediction data and reference data. (**a**), Multilayer Perception; (**b**), Random Forest; (**c**), Adaboost; (**d**), Gradient Boosting Decision Tree; (**e**), XGBoost; (**f**), LightGBM. Based on the analysis results of Table 3 and Figure 5, it can be considered that LightGBM is the model with the best overall performance among the six machine learning models in the grassland GPP prediction in this study.
## 2 Materials and Methods  
### Device Overview  
In this section, the overall design of the proposed device of sensing is introduced. For the implementation, the following needs have been considered and analyzed:  
* The design of a compact mechanical concept;
* A circuit that integrates the functional components to detect color and proximity;
* A machine learning model that allows the classification of tissues based on their color;
* A BLE (Bluetooth Low Energy) communication to allow the interconnection of the various arms and the introduction of the IoT to surgical robotics.  
The main purpose of the first prototype proposed in this manuscript is to carry out the sensorization of the surgical instrument used during procedures with surgical robots, to ensure a safer interaction with the organs. For the first prototyping phase, an Arduino Nano 33 BLE Sense board is used, due to its compact size and the presence of integrated sensors. Reference is made to the colorimetric and proximity sensor integrated inside the APDS9960 unit of the Arduino board considered. In particular, the proximity sensor provides feedback on the distance between the tip of the surgical instrument and the organs, to ensure safe interaction. In addition, the colorimetric feedback allows, with the implementation of a simple neural network, to identify specific tissue areas with characteristics that are not typical of physiological tissue, such as cancerous tissue structures. This kind of recognition leads the way, in the future, to the classification of healthy tissue and diseased tissue. A 3D printed mechanical support is used to mount the electronic board to the robotic instruments, consisting of two main parts held together using a magnetic anchor. The magnets are integrated into the mechanical structure of the device, and allow for a quick alignment of the two parts, and a stable anchoring to the instrument. The dimensions of the holder are compatible with the size of conventional da Vinci EndoWrist tools; as this device of sensing is designed for robot-assisted laparoscopic surgery, it can be integrated into instruments of the Patient Side Manipulator (PSM) of the da Vinci Research Kit (dVRK) and can be teleoperated by the Master Tool Manipulator (MTM) [12] (Figure 1).
## 4 Discussion
### PV Cooling Techniques  
Three different cell cooling techniques integrated into a typical PV module were examined with the aim of assisting in the zero-energy transformation of buildings. Their energy performance was strongly dependent on the climatic conditions of the location examined. Despite the slight reported efficiency enhancement, the incorporation of the respective cell cooling techniques can have a decisive impact on the self-sufficiency of a building's operation. The findings of the present analysis are supported by the existing literature, which verifies the integrity of the calculations of the study.  
Firstly, regarding the technological solution of the radiative PV, the results obtained from the present study report a performance enhancement that lies between 0.52%, the poorest performance for the city of Stockholm, and 0.71%, the highest performance for the city of Munich. Similar studies also indicated a slight enhancement in the overall performance of photovoltaic systems through the incorporation of the optically advanced coating solution. Specifically, [PERSON] et al. [15] examined the solution of silica multifunctional hybrid structures integrated with radiative cooling coating, and they reported only a marginal improvement of 0.53% regarding the application on solar cells. Similarly, in the study of [PERSON] et al. [16], the percentage of performance increase attained by the implementation of a radiative coating on a PV module was restricted between 0.43% and 1.07%.  
Secondly, the incorporation of PCM in the PV was found to be beneficial only for the hotter climate of Athens, leading to a marginal enhancement of 0.63%. However, the respective solution had an adverse effect on the PV performance for the cities of Barcelona, Munich and Stockholm. Similar conclusions were drawn by [PERSON] et al. [20] who concluded that the PCM solution was not always advantageous for the PV performance. Additionally, [PERSON] et al. [17] observed that after a prolonged operation, the PCM-PV system resulted in higher cell temperatures in comparison with the conventional PV cell, which indicates that the use of PCM is not always effective. Lastly, as far as the finned PV construction is concerned, the reported performance enhancement was the highest among the PV cell cooling techniques, lying between 0.58% and 0.89%. The experimental work of [PERSON] et al. [21] concluded that the applications of fins in the backside of the PV can result in performance enhancements of up to 2%. However, these enhancements are dependent on weather conditions, and therefore in some cases, the simple PV configuration is more efficient in contradiction to the finned PV. Also, the specific design of the fins has been found to play a role in the final performance, according to the literature.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 3 Matrix Applications in Signal Estimation Theory
### Matrix Inverse Lemma for Derivation of the Recursive Least-Squares Filter
Despite the fundamental role played by the RLS algorithm in the adaptive signal processing field, it encounters numerical problems when implemented in a finite precision environment, and especially in fixed-point arithmetic DSP processors [15]. The round-off error propagation, defined as the influence of subsequent computations when a single perturbation occurs at a random iteration, represents one of the major drawbacks of the RLS algorithm in a finite precision implementation and has been well studied in literature [16; 17; 18]. A popular method to improve the accuracy and stability of RLS algorithm is to implement a variable forgetting factor (VFF) [19; 20; 21]. Other modified RLS methods can be found in [22; 23; 24; 25] and the references therein.
## 2 SLAM Based on Multi-Sensor Fusion
### Data Fusion Based on EKF  
When a depth camera or LiDAR is used alone for map construction, there will be a certain error due to a single sensor defect. Therefore, this paper used the EKF to fuse the depth camera data and LiDAR data for mapping to improve the accuracy of the mapping. When using the extended Kalman filter for data fusion, the measurement accuracy of LiDAR is higher than that of the depth camera, and thus the measurement value of the depth camera is taken as the system prediction value at the current moment, and the measurement value of LiDAR is taken as the system measurement value, so as to ensure the accuracy of data fusion. The process of EKF data fusion is as follows:  
1. Forecast section. First, initialize the current position and covariance error with the initial position of the robot. Use the depth camera on the vehicle to collect data on vehicle motion. This value is used as the current moment system input \(u_{k}\) to predict the pose of the next moment, and the predicted error covariance is calculated. Predict \(x_{k|k-1}\) according to the current pose: \[x_{k|k-1}=f\Big{(}x_{k-1|k-1},u_{k}\Big{)}\] (5) \[P_{k|k-1}=A_{k}P_{k-1|k-1}A_{k}^{T}+Q_{k}\] (6)  
Figure 2: Schematic diagram of depth-to-laser conversion.  
where \(A_{k}\) is the Jacobian matrix of the state equation \(A_{k}=\frac{\partial\tau}{\partial x}\Big{|}_{x_{k|k-1},\mu_{k}}\), and \(Q_{k}\) represents the covariance matrix of the Gaussian noise of the predicted state.
2. Data association. Before the data fusion of the depth camera and the LiDAR, the data of the two sensors need to be correlated to prevent the mis-matching of the sensor data and the inability to build a complete map. According to the Mahalanobis distance between the observation information obtained by the sensors, the correlation of the data obtained by the two sensors is judged. It indicates that the data correlation is high when the distance is less than the set threshold \(x^{2}\). \[D_{ij}^{2}=\tau_{t}^{T}P_{k|k-1}\tau_{t}<x_{d,n}^{2}\] (7) \[\tau_{t}=Z_{l}-Z_{c}\]  
where \(d=rank(\tau_{ij})\), \(\alpha\) is the reliability, \(\tau_{t}\) is the measured position error, \(Z_{l}\) is the measured value of the LiDAR, and \(Z_{c}\) is the measured value of the depth camera.
3. Update section. First, calculate the Kalman gain \(K_{k}\), and use the measured values of the multi-sensors to correct the previous system predictions, that is, to use the position information obtained by the LiDAR to correct the positioning data obtained by the camera for data fusion. At the same time, the optimal estimation value of sensor data fusion is calculated by Kalman gain, and the update of the covariance matrix is completed.  
\[K_{k}=P_{k|k-1}H_{k}^{T}\Big{[}H_{k}P_{k|k-1}H_{k}^{T}+R_{k}\Big{]}^{-1} \tag{8}\]  
\[x_{k|k}=x_{k|k-1}+K_{k}(z_{k}-h(x_{k|k-1})) \tag{9}\]  
\[P_{k|k}=(I-K_{k}H_{k})P_{k|k-1} \tag{10}\]  
where \(H_{k}\) is the transformation matrix, and \(R_{k}\) represents the covariance matrix of the Gaussian noise of the measurement.
## 3 Results
### Annual and Seasonal Pattern of FPAR: Comparing MOD15A2 with Tower  
MOD15A2 FPAR provides a key and seasonally-varying input to the MODIS GPP algorithm, and is derived from remote sensing. As FPAR is also measured on site with no effect from atmospheric components, we tested for its accuracy in the MODIS GPP calculation. The FPAR estimates from MODIS ranged from 0.75 to 0.92, in fair agreement with the corresponding measurement at the tower with values ranging from 0.77 to 0.97. The seasonal pattern is moderately correlated between the two datasets (Figure 2 and Table A1), with an average relative error (RE = 100 \(\times\) (MODIS - EC)/EC) of 6%. REs reached up to 16% in autumn or winter. This low seasonality represented by FPAR for this type of evergreen forest was also evidenced by [37]. The tower FPAR pattern decreases in mid-summer and reaches a maximum in winter; in contrast, the MODIS FPAR exhibits a reversed seasonal pattern, with the maximum FPAR reached in summer. Indeed, MODIS FPAR displays good accuracy of prediction  
Figure 2: Comparison between the measured flux-tower FPAR and MOD15A2 FPAR from 2001 to 2010 (points represent means \(\pm\) SE). Blue lines represent MODIS FPAR, red lines represent tower FPAR and the dashed lines are the dividing line of the season: spring (DOY 65–152), summer (DOY 153–248), autumn (DOY 249–335) and winter (DOY 0–64 and 336–366).  
during the spring and summer periods with RE varying between 0 and 9% (Table A1). The annual maximums of RE are all concentrated in winter when GPP is low (due to low air temperatures during this period which leads to low photosynthetic activity), showing that errors in FPAR MODIS are a weak contributor to anomalies in GPP MODIS.
## 3 Line-Scan Hyperspectral Imaging Instruments  
### Major System Components  
#### 3.1.1 Light Sources  
Illumination and excitation sources are two major types of light sources used in line-scan hyperspectral imaging systems. Broadband lights are commonly used illumination sources, and their spectral constitutions are not changed during light-sample interactions. Quartz tungsten halogen (QTH) lamps produce a smooth spectrum in a broad wavelength range from visible to infrared, and have been widely used as illumination sources in various reflectance and transmittance imaging measurements. Broadband light-emitting diodes (LEDs) have also been used to illuminate food and agricultural products for different hyperspectral applications. On the other hand, narrowband lights are usually used as excitation sources. High-intensity monochromatic light can excite biological materials during light-sample interactions to generate low-intensity light in a broad wavelength range (e.g., fluorescence emission and Raman scattering). Lasers are powerful excitation sources ideal for fluorescence and Raman measurements owing to their highly concentrated energy, perfect directionality, and true monochromatic emission. Narrowband LEDs are emerging excitation sources that offer many advantages such as long lifetime, low power consumption, low heat generation, small size, fast response, robustness, and non-sensitivity to vibration. Other light sources--such as ultraviolet (UV) fluorescent lamps, high-pressure arc lamps, and low-pressure metal vapor lamps--can also be used for excitation purposes.  
Line-scan hyperspectral imaging systems generally have a line-shaped instantaneous field of view (IFOV). Area and line lights from illumination or excitation sources can be projected on the samples to cover the linear IFOV for line-scan surface evaluations. Point sources, which are usually generated using optical fibers and focus lenses, are required in line-scan spatially resolved spectroscopy measurements for subsurface evaluations, since spectral data need to be collected from a series of positions laterally away from the source. Figure 2 shows line laser and point laser excitation sources used in a line-scan hyperspectral Raman system. The 785 nm line laser (Figure 2a) with a maximum power of 5 W is created by spreading a high-intensity laser beam using a scanning mirror. The laser line, normally projected on the sample surface via a 45\({}^{\circ}\) dichroic beamsplitter, is about 1 mm wide and 24 cm long at a vertical distance (i.e., from beamsplitter to sample) of 28 cm. The line laser serves as the excitation source for line-scan Raman imaging measurements. On the other hand, the 785 nm point laser (Figure 2b) is delivered and focused on the sample surface using an optical fiber and a laser focus unit. The laser point is projected on the sample surface with an incident angle of 30\({}^{\circ}\), and is approximately 1 mm in diameter with a maximum power of 350 mW. The point laser is used in line-scan spatially offset Raman spectroscopy measurements.
# Bird Detection on Power Transmission Lines Based on Improved YOLOv7
## 4 Experimentation and Analysis
### Evaluate the Impact of ODConv on Model Accuracy  
To assess the impact of the ODConv, we conducted ablation experiments by substituting SE and ECA modules for the ODConv modules. The SE module primarily encompasses squeeze and excitation operations.  
The squeeze and excitation (SE) mechanism is a dynamic channel attention technique crafted to enhance the feature representation within convolutional neural networks (CNNs).  
Figure 5: Incorporating the YOLOv7 network structure into ODConv.  
It tackles CNN limitations by adaptively recalibrating the importance of different channels within a feature map. The SE mechanism comprises two pivotal operations: squeeze and excitation. In the squeeze operation, global spatial information is obtained by conducting global average pooling on the feature map, which results in a channel descriptor. Subsequently, the excitation operation employs this descriptor to generate channel-wise weights through a set of learnable transformations. These weights effectively modulate the original feature map, accentuating pertinent channels while suppressing less informative ones. This mechanism significantly bolsters the discriminative capacity of features and elevates model performance across a spectrum of tasks.  
The efficient channel attention (ECA) module is another attention mechanism tailored to improving feature learning in CNNs while being computationally efficient. ECA operates by exploiting the interdependencies among channels in a feature map. Instead of using global pooling, ECA employs a 1D convolutional operation along the channel dimension, capturing channel-wise dependencies. The resulting attention map is obtained by applying a softmax function to the convolutional output, and it is then used to re-weight the feature map. ECA amplifies information dissemination among channels, empowering the network to prioritize vital features while discarding extraneous ones, all without imposing substantial computational overhead. This makes ECA particularly suitable for resource-constrained scenarios where efficiency is crucial, while still providing performance gains by effectively integrating contextual information within feature representations.
###### Abstract  
Self-excited thermoacoustic instability (SETAI) is an undesirable and dangerous phenomenon in combustion systems. However, its control is difficult, thus greatly limiting the development of combustion technology. Our previous works clarified how the premixed chamber length (\(L_{\text{P}}\)) and equivalence ratio (\(\varphi\)) influence SETAI behavior in a symmetrical hedge premixed combustion system. On real-world sites, however, the supply structure or combustion condition in a multi-flame system could be asymmetric due to space limitations or combustion adjustment needs. This paper aims to clarify the SETAI behavior of a combustion system with an asymmetric supply structure or an asymmetric combustion condition. The results indicate that the sound pressure amplitude under strong oscillation can reach 160 dB, which is about 5% of the total pressure. The SETAI state under the asymmetric condition is determined by the coupling between the heat release oscillation and sound pressure oscillation on each side and their cooperation. The asymmetric supply structure leads to asynchronous heat release oscillations between the two sides; it may be that one promotes oscillation and that the other suppresses it, or that both have a promotion effect but with asynchronous action, thus partly canceling each other out to lower the system's oscillation intensity. This brings an advantage for controlling SETAI, which can be achieved by only changing one side of the structure. The oscillation amplitude can be reduced by 80-90% by appropriately changing one \(L_{\text{P}}\) only by \(\sim\)20%. Under an asymmetric combustion condition with \(\varphi\) differing between the two sides, the heat release oscillation on each side is dependent on the local \(\varphi\) but not the global \(\varphi\). Consequently, SETAI can also be controlled by changing the distribution but maintaining a constant fuel feeding rate and \(\varphi\). The concepts identified in this paper demonstrate that SETAI can be effectively controlled by adopting an asymmetric \(\varphi\) distribution or an asymmetric structure of the supply system. This provides a convenient SETAI control approach without affecting the equipment's thermal performance.  
thermoacoustic instability; acoustic waveform; asymmetric condition; supply structure; combustion equivalence ratio +
Footnote †: journal:  
0203
# On the Semantics of Hybrid ASP Systems Based on Clingo
## 4 Structured Theories and Answer Sets
### Structured and Compositional Theories
We say that a theory \(\mathfrak{T}=\langle\mathcal{T},Sat,\widehat{\cdot}\rangle\) structured by \((\mathcal{X},\mathcal{D},\mathit{vars},\llbracket\cdot\rrbracket)\) is _compositional_ if \(Sat=\{S\subseteq\mathcal{T}\mid\llbracket S\rrbracket\
eq\emptyset\}\). Therefore, a set \(S\) is \(\mathfrak{T}\)-satisfiable iff its denotation is not empty. As an example, let us associate the theory of linear equations \(\mathfrak{L}\) with the structure \((\mathcal{X}_{\mathcal{L}},\mathcal{D}_{\mathcal{L}},\mathit{vars}_{\mathcal{L }},\llbracket\cdot\rrbracket_{\mathcal{L}})\), where  
* \(\mathcal{X}_{\mathfrak{L}}\) is an infinite set of integer variables,
* \(\mathcal{D}_{\mathfrak{L}}=\mathbb{Z}\),
* \(\mathit{vars}_{\mathcal{L}}(\&\mathsf{sum}\{k_{1}\ast x_{1};\ldots;k_{n}\ast x _{n}\}\prec k_{0})=\{x_{1},\cdots,x_{n}\}\), and
* [&sum\{k_{1}*x_{1};\ldots;k_{n}*x_{n}\}\prec k_{0}\,]_{\mathfrak{L}}= \{v\in\mathcal{V}_{\mathfrak{L}}\ \mid\ \{k_{1},v(x_{1}),\ldots k_{n},v(x_{n})\}\subseteq \mathbb{Z}\text{ and }\sum_{1\leq i\leq n}k_{i}*v(x_{i})\prec k_{0}\}.\]  
With \(\mathfrak{L}\), a set \(S\) of theory atoms capturing linear equations is \(\mathfrak{L}\)-satisfiable whenever \([\![\,S]\! ]_{\mathfrak{L}}\) is non-empty. Once \(\mathfrak{L}\) is structured in this way, we can establish the following properties. Theorem 4.2: _Theory \(\mathfrak{L}\) structured by \((\mathcal{X}_{\mathfrak{L}},\mathcal{D}_{\mathfrak{L}},\text{vars}_{\mathfrak{ L}},[\![\,\cdot\,]\! ]_{\mathfrak{L}})\) is compositional and consistent._  
If a theory \(\mathfrak{L}\) is compositional, we can define an associated entailment relation as follows. Definition 4 (Compositional entailment): Let \(\mathfrak{T}=\langle\mathcal{T},\text{Sat},\widehat{\,\,\,\,\,\,\,\,\,\,\,\, \