esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 4 Discussion  
### Response of Carbon Fluxes to Gradient Warming  
Generally, warming affects carbon fluxes by altering TS, SWC, plant photosynthesis, and microbial activities [28; 29; 30]. In our study, an increased TS led to a decrease in GPP and an increase in RE, and this altered the absolute values of NEE (Figure 5). Specifically, warming significantly reduced GPP by decreasing the SWC in the Zoige peatland (Tables 1 and 3), a result that is consistent with observations from alpine grasslands in Nam Co, QTP, where elevated temperatures were associated with reduced GPP due to intensified drought conditions and suppressed vegetation growth [11]. Another study at the interface of the QTP, Loess Plateau, and Mongolian Plateau also found that rising temperatures were a major factor in deducing GPP by increasing grassland aridity and inhibiting vegetation growth in grassland ecosystems [31]. These findings suggest that warming primarily affected the GPP by suppressing water availability in QTP grasslands [32]. Additionally, the non-significant increase in RE in the TL and TH treatments could be attributed to increased microbial activity [33]. Overall, the warming-induced reduction in NEE in our study was mainly attributed to the decreased GPP, as warming only significantly affected GPP, not RE (Table 3), which is consistent with a study conducted in an alpine meadow in Haibei, QTP [34]. These findings suggest that the GPP is the main contributor to changes in NEE under warming conditions.  
Figure 5: Pearson correlations among the soil water content, soil temperature, carbon and water fluxes, and water use efficiency. “\(\
u\)”, and “\(\
u\)” represent significant effects at \(p<0.05\), \(p<0.01\), and \(p<0.001\), respectively. SWC, soil water content; TS, soil temperature; GPP, gross primary productivity; RE, ecosystem respiration; NEE, net ecosystem carbon exchange; E, soil evaporation; T, plant transpiration; ET, evapotranspiration; WUEc, canopy water use efficiency; WUEe, ecosystem water use efficiency; T/ET, ratio of soil evaporation and evapotranspiration.
## 3 Models for Parameters' Identification and UFLS Schemes' Optimization
### Parameter Identification Strategy for Simplified SFR  
It is well known that accurate parameters are necessary for the design of the protection scheme and parameter identification techniques based on measurements have become mature after many years of development. Accordingly, the parameter identification method for the simplified SFR model is proposed in this section.  
For a model with unknown parameters but a known model structure, the parameter identification problem can be regarded as an optimization in which the model output is compared with the actual output. In order to evaluate the parameters to be identified, the actual output is compared with the model output. Generally, the objective function can be defined as a weighted quadratic function:  
\[\begin{array}{rl}\min g(x)=\frac{1}{m}\sum\limits_{i=1}^{m}\int_{t}\left(y_{ i}-y_{i}^{s}\right)^{T}W(y_{i}-y_{i}^{s})\\ s.t.\quad lb\leq x\leq ub\end{array} \tag{5}\]  
where \(g(.)\) denotes the objective function, \(W\) is a positive definitive weight matrix, \(y\) is the model output, \(y^{s}\) is the actual output, \(x\) is the vector of unknown parameters to be identified and \(m\) is the number of recorded data sets.  
In the proposed method, the objective function developed as a measure of how the simplified SFR model output satisfies the measurement of the full scale power system output. Then the parameter identification method for the simplified SFR model is formulated as an optimization model:  
\[\begin{array}{rl}\min(x)=\frac{1}{m}\sum\limits_{i=1}^{m}\sum\limits_{k=1}^{ n}\left(f_{i}(k)-f_{i}^{s}(k)\right)^{2}\\ s.t.\quad lb\leq x\leq ub\end{array}, \tag{6}\]  
The objective function (6) is built with the system frequency response at the center of inertia (COI) [29], and \(x=[H\,D\,K\,\,T]^{T}\) are the four unknown parameters to be identified, \(f_{i}\) and \(f_{i}^{s}\) are the system frequency response at the COI of the full scale system output and that of the simplified SFR model output, respectively, and \(n\) is the number of sampled data.  
So far, the parameter identification problem for the simplified SFR model has been transformed into an optimization model.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 2 Manufacturing Process
### Design for Manufacturing  
In order to enable an AM process, the standard geometry, exhibiting completely enclosed sidewalls, of a WR12 waveguide was slightly modified to improve subsequent post-processing and the metallization processes. Therefore, non-radiating slots were inserted in the side walls as pointed out in Figure 1a-c. The slots are oriented parallel to the direction of the current density and thus do not affect the spatial field distribution. The slot dimensions were already optimized in previous works of the authors [14]. In order to provide a mounting to a vector network analyzer (VNA) with E-Band converters, the waveguides (Figure 1a-c) can be inserted in a fixture (Figure 1d) which provides additional flanges.
## 4 Hierarchical Cooperative MIMO Clustering
### Design Considerations
#### 4.5.2 Factors Affecting Routing  
In the proposed scheme, we forward data to the CCS via hierarchical routing (Figure 2). The main aim of hierarchical routing is to efficiently preserve the energy of sensor nodes by involving them in multihop communication and performing early data aggregation in order to decrease the number of transmitted messages to the CCS. Each cluster is associated with a routing cluster. When a cluster gets associated with a routing cluster, it calculates the amount of energy required to send data to the routing cluster. It then send a PING message to CCS. If cost of sending PING message is higher or if CCS is not in the transmission range, cluster adds its routing cluster as next hop \"node\" in its routing table. In case CCS is in the close vicinity of the cluster, CCS becomes next hop neighbour.
## 4 Results
### Smart Sensing Operation  
As has been described, all the sensors include communications capabilities, smart processing based on low-cost hardware and cloud computing facilities for predictions and cooperative work among different BTSs.  
Going one step further, a set of controllable MG-based BTSs can be aggregated through a centralized management in the shape of a Virtual Power Plant (VPP), which represents a controllable portfolio of BTSs. Consequently, from a VPP perspective, each MG-based BTS is seen as an aggregated controllable VPP node which can interact with other VPP nodes, i.e., other BTSs, with the aim of facilitating the integration of the BTSs into the grid.  
Therefore, a hierarchical network structure with a hierarchical control strategy involving both concepts, i.e., microgrid and VPP, can constitute a feasible solution to the challenge of coordinating several BTSs to improve performance.  
To allow this cooperative operation mode, the sensors included in the MG-based BTS could provide the EMS system with irradiance predictions in order to better adjust the load and the BESS connection strategy [56]. These predictions are possible because the data provided by the different sensors is stored and processed both locally and in the cloud, thus constituting an intelligent sensor strategy. Additionally, in installations where the PV power installed is greater than the amount of load demanded by the BTS, based on power predictions, a new battery state could be considered where the BESS is also charged during the PT period with the energy surplus from the PV system.
## 4 Concept of a DAid(r) Smart Socks-Based Biofeedback System
### System Requirements and Data  
The system relies on several core services and modules to ensure seamless data transmission, processing, and storage. The BluetoothService manages all Bluetooth communication with the DAid(r) smart socks. Its primary function is to scan for available devices, establish connections, and continuously read data from the smart socks. The service handles potential disconnections by implementing reconnection logic to ensure the system remains operational during training sessions. Key methods include connecting to the smart socks via a specific Bluetooth address and continuously retrieving sensor data in real-time. The MRCommunicationService is responsible for sending processed data to the HMD over a Wi-Fi network. Once the sensor data are received and processed by the system, they are converted into a MR-compatible format before being transmitted to the HMD. This service ensures the reliability of data transmission to maintain the accuracy and consistency of real-time feedback during the training sessions. The DataProcessingModule is tasked with analyzing raw data collected from the smart socks. It involves three key steps: data parsing, movement analysis, and data aggregation. The system includes a local storage solution for storing both raw and processed data. The storage system is structured in a database with two primary tables: Sessions, which holds metadata about each training session (e.g., start time, duration), and SensorData, which contains both raw and processed sensor data linked to specific sessions. This allows for later retrieval and analysis, providing users with the ability to review past performance and monitor progress over time. These services and modules work together to ensure that sensor data are accurately captured, processed, and transmitted to the MR environment.
# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 2 Relevant Theories
### Detection of Dynamic Feature Points by LK Optical Flow Method  
The optical flow method is a method that describes the movement of pixels between images over time. Considering the real-time nature of the system, the ORB feature points extracted by the SLAM system are calculated based on the Lucas-Kanade (LK) optical flow method. In the LK optical flow method, the image can be regarded as a function of time, and the gray level of a feature point located at \((x,y)\) at moment t can be written as \(I(x,y,t)\).  
By the grayscale invariance assumption:  
\[\frac{\partial I}{\partial x}\frac{dx}{dt}+\frac{\partial I}{\partial y}\frac {dy}{dt}+\frac{\partial I}{\partial t}=0 \tag{1}\]  
where \(dx/dt,dy/dt\) is the motion velocity of the feature point in the \(x,y\) direction, denoted as \(u,v\), respectively; \(\partial I/\partial x,\partial I/\partial y\) is the gradient of the feature point in the \(x,y\) direction, denoted as \(I_{x},I_{y}\), respectively; and the change of the gray value of the feature point against time is denoted as \(I_{t}\). Equation (1) is rewritten as:  
\[\begin{bmatrix}I_{x}&I_{y}\end{bmatrix}\begin{bmatrix}u\\ v\end{bmatrix}=-I_{t} \tag{2}\]  
Figure 2: The overall architecture of DeepLabv3+ models.  
To calculate the motion velocity \(u,v\) of the feature point, other constraints are added. Suppose the pixels in the same region have the same motion, i.e., taking an \(8\times 8\) square region centered on the feature point and all 64 pixels in the region having the same motion, then the equation is:  
\[\begin{bmatrix}I_{x}&I_{y}\end{bmatrix}_{k}\begin{bmatrix}u\\ v\end{bmatrix}=-I_{t_{k}},k=1,2,\ldots,64. \tag{3}\]  
Note that:  
\[\mathbf{A}=\begin{bmatrix}\begin{bmatrix}I_{x},I_{y}\end{bmatrix}_{1}\\ \vdots\\ \begin{bmatrix}I_{x},I_{y}\end{bmatrix}_{k}\end{bmatrix},b=\begin{bmatrix}I_{t 1}\\ \vdots\\ I_{k}\end{bmatrix} \tag{4}\]  
Solve by the least squares method:  
\[\begin{bmatrix}u\\ v\end{bmatrix}^{*}=-\begin{pmatrix}\mathbf{A}^{T}\mathbf{A}\end{pmatrix}^{-1}\mathbf{A}^{T }b \tag{5}\]  
Suppose the total number of feature points within an image frame is \(n\), and the average motion velocity of all feature points is calculated according to Equation (5) as:  
\[\begin{bmatrix}U\\ V\end{bmatrix}=\frac{1}{n}\sum_{k=1}^{n}\begin{bmatrix}u_{k}\\ v_{k}\end{bmatrix},k=1,2,\ldots,n \tag{6}\]  
where \(U,V\) is the average velocity of the feature point along the \(x,y\) direction, respectively. \(u_{k},v_{k}\) is the velocity of the \(k\)th feature point along the \(x,y\) direction, respectively.  
All feature points within a frame can be filtered by comparing the average motion velocity, as shown in Equation (7):  
\[\sqrt{\begin{bmatrix}\left(u_{k}-U\right)^{2}+\left(v_{k}-V\right)^{2}\end{bmatrix} }>d,k=1,2,\ldots,n \tag{7}\]  
where \(d\) is the judgment threshold.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 1 Introduction
They show promising performance on tackling the class imbalance problem, because the class-wise loss value is dynamically weighted by the number of pixels. Although region-based loss proves successful in many tasks, it aggregates point-wise loss in a class-specific way, which is incompatible with methods that tackle other point-wise loss imbalance problems, such as boundary imbalance and easy-hard example imbalance introduced in Section 2.2. [PERSON] et al. [13] propose a universal loss re-weighting strategy to dynamically balance the class-wise loss value, sharing the same idea behind IoU loss. However, the efforts to mitigate easy-hard example imbalance and boundary imbalance still remain blank. Other recently proposed loss functions are based on prior knowledge about the shape [14], boundary [15], or pixel distance of the target [16]. They provide different perspectives to value the contribution of each point to segmentation accuracy, but they do not solve loss imbalance directly. A novel balanced loss function for surface defect segmentation is proposed in this paper to mitigate the loss imbalance problem and improve segmentation accuracy. Our method combines three strategies that address three identified loss imbalance types, respectively: dynamical class weighting against label imbalance; truncated cross-entropy loss against easy-hard example imbalance; and label confusion suppression against boundary imbalance. The balanced loss function is applicable to any segmentation model. We evaluate the effectiveness of the balanced loss function on KolektorSDD2 [17], the Severatal dataset [18] and a self-collected aluminum alloy surface defect dataset. The result shows that the balanced loss function can improve segmentation accuracy (IoU) on various benchmarks and networks by a large margin, from 5% to 30%. To summarize, the contributions of this paper include:  
* An in-depth investigation is performed into the loss imbalance problem in the training of the surface defect segmentation model, namely label imbalance, easy-hard example imbalance, and boundary imbalance. * A novel balanced loss function is proposed to mitigate the loss imbalance problem by dynamical class weighting, truncated cross-entropy loss, and label confusion suppression. * Extensive experimental evaluations show that the balanced loss outperforms various common loss functions on surface defect segmentation benchmarks.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 3 Results
### Characteristics of the Ionospheric Delay  
Given the significant impact of the ionospheric delay on the X-band satellites, including TerraSAR-X and Tianhui-2, we primarily focus on the numerical characteristics of the ionospheric delay. Given that SAR satellites are typically in dawn-dusk or noon-midnight orbits, we collected the VTEC of the study area at 12 PM and 6 PM local time in 2019 (Figure 14a), finding that the variation ranges of the two moments are approximately the same, with a maximum value of around 20 TECU. Owing to direct sunlight in the tropics, the average VTEC in that area is more significant than in the study area (Figure 14b), reaching up to 25 TECU.  
We used the SAR satellites TerraSAR-X, Sentinel-1, and ALOS-2 across three signal bands as examples and calculated the ionospheric delay when the VTEC was 25 TECU and 0 was 45\({}^{\circ}\) ((Equation (5); Table 2). Generally, the ionospheric delay for X-band satellites is comparable in magnitude to the residual of the tropospheric correction and, thus, minimally impacts the geolocation accuracy, rendering ionospheric correction unnecessary. The variation in the ionospheric delay is even smaller (discussed in Section 3.1). For the C-band, the ionospheric delay is usually less than 1 m. The accuracy of the ionospheric correction is acceptable even if a residual remains. However, in the L-band, among all factors, the ionospheric delay is the most significant influence on geolocation, potentially causing an error as high as 10 m. Employing higher-resolution data such as Jet Propulsion Laboratory High-Resolution Global Ionospheric Maps (JHR) enhances the positioning precision significantly [22].
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 1 Introduction
### The Present Study  
The study sought to analyze costs and benefits for recall completeness and accuracy of three different experimental conditions: (1) individual, (2) nominal and (3) collaborative condition, using the lists of the DRM paradigm [11], in a VE recently implemented by our lab [54; 55; 56]. We focused on the stimulus materials (i.e., list of concrete versus abstract words), the effect of the group size (i.e., individual, dyad, triad, and quartet), and the experimental conditions order (i.e., individual-nominal vs. nominal-individual or nominal-collaborative vs. collaborative-nominal) on the performance. We also investigated the impact of anxiety state and personality traits as well as gender group composition (i.e., same-gender, mixed-gender and gender majority group) on the performance. Finally, we analyzed the social interactions among members of collaborative groups within the VEs.  
No previous literature extended the DRM paradigm to study group collaborative inhibition and facilitation within VEs. And no one compared minimal and not minimal group dynamics in order to capture the role of mental schemes and cognitive heuristics which lays beyond the actual communication dynamics. In order to take into account the complexity of real group interactions, we decided to consider all the fundamental experimental factors characterizing the task to build the experimental hypotheses. In particular, the main aim of the study is to verify the magnitude and dynamics of well known cognitive processes (i.e., collaborative inhibition and facilitation) within VEs. A way to answer to this research question is to verify the expectations derived from literature reporting evidences coming from \"real\" environments (i.e., FtF and classical studies). The following hypotheses have been formulated as extensions of findings coming from real environments, or from different virtual dynamics not related with the DRM paradigm. Nonetheless every hypothesis has never been tested, and reported as follows with sorted by importance:  
1. The collaborative facilitation and inhibition, during a DRM task, are present even within a VE (e.g., [2; 8; 12; 13]).
2. The list of abstract words will be more difficult to learn than the list of concrete words even within a VE [14].
3. False recall will increase in relation to group size [17].
4. Prior collaboration will enhance later nominal studied word recognition (e.g., [1; 7]).  
Participants with lower levels of anxiety will perform better than people with higher levels of anxiety, and personality traits will not influence the performance [21].
* Females will have a better performance in all experimental conditions and they will have a better performance in the gender majority group, while males will perform better in mixed-gender groups (e.g., [18; 32]).
* A greater involvement of participants in the chat will produce better performance in a DRM task [48].
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 3 Materials and Methods
### Proposed ANN-Based Rotational Speed Control
#### 3.1.1 Grid-Side Converter Control  
The vector control based on Voltage Oriented Control has been adopted for the GSC therefore the \(d\)-axis is aligned with the grid voltage vector [29]. Hence \(v_{dg}=v_{g}\) and \(v_{qg}=0\). Thus, the grid's active and reactive powers may be written as:  
\[\left\{\begin{array}{l}P_{g}=\frac{3}{2}v_{dg}i_{dg}\\ Q_{g}=-\frac{3}{2}v_{dg}i_{qg}\end{array}\right. \tag{28}\]  
Thanks to the Voltage Oriented Control the equations of voltages across the grid are:  
\[\left\{\begin{array}{l}v_{dg}=R_{g}i_{dg}+L_{g}\frac{di_{dg}}{dt}\overbrace{ \omega_{g}L_{g}i_{qg}}^{\Delta v_{dg}}+u_{dg}\\ 0=R_{g}i_{qg}+L_{g}\frac{di_{dg}}{dt}\overbrace{+\omega_{g}L_{g}i_{dg}}^{ \Delta v_{dg}}+u_{dg}\end{array}\right. \tag{29}\]  
where \(\Delta v_{dg}\) and \(\Delta v_{qg}\) are feed-forward compensation terms which are added back to the control signals.  
Figure 10 shows the control scheme used to regulate the DC-link voltage and reactive power. The control scheme consists of two rows of two PI controllers in series.  
The outer loops regulate the grid currents and generate the reference currents \(i_{dg}^{*}\) and \(i_{qg}^{*}\) using PI controllers. The PI controllers receive as reference inputs both the grid's reactive power \(Q_{g}^{*}\), frequently set to zero, and the reference DC voltage \(V_{DC}^{*}\) = 800 V. The obtained \(i_{dg}^{*}\) and \(i_{qg}^{*}\) currents are the reference inputs of the inner loops' PI controllers. The inner loops generate reference voltages \(v_{dg}^{\prime}\) and \(v_{qg}^{\prime}\) which are compensated to get the required \(v_{dg}^{*}\) and \(v_{qg}^{*}\) for the Pulse Width Modulation.  
Figure 10: Grid-side converter control scheme.
## 4 Results and Analysis
### WASN-Based Analysis in Real-Life Scenarios  
We deployed a WASN consisting of 50 sensor nodes to monitor environmental noise and acoustic event in the campus. As shown in Figure 10, these sensor nodes were deployed at main roads, teaching buildings, and gates, and we show the actual deployment of ten nodes. It should be specially explained that all the nodes were fixed on lamp poles or rails by metal hoops. In addition to the noise monitoring, it is significant for us to choose car horn as the acoustic event because the campus is a forbidden area. All data were transmitted to the server and saved in the database.
## 28 nm CMOS Technology Consideration  
Nanoscale CMOS technology has the advantage of allowing high-frequency circuit design and low power operation due to the increase in the transition frequency (\(F_{t}\)). For analog and digital circuits, the transistor's \(F_{t}\) and low power characteristics are important factors in the design. Bandwidth and gain limiting characteristics, which are the performance degradation of analog circuits, are a difficult factor in designing an amplifier, and noise leads to many limitations in digital design. Of course, performance degradation due to intrinsic parasitic components of the transistor layout is large, but it can be overcome by scaling technology of CMOS to reduce parasitic components and operating voltage [16].  
Figure 1 shows representative RF parameters such as \(F_{t}\), \(F_{max}\) (maximum oscillation frequency), and \(NF_{min}\) (minimum noise figure) according to length of transistor. Here, the \(F_{max}\) in Figure 1a increases dramatically, and the \(F_{t}\) of 28 nm CMOS process has a high transient frequency of 142 GHz. For the same dc-current consumption of 8 uA, \(NF_{min}\) decreases with the gate length scaling as shown in Figure 1b, due to the relationship of \(f\!/F_{t}\) where \(f\) means frequency of interest. The scaling CMOS technology improves the operation frequency due to the reduced bias and the scaling CMOS [16]. Therefore, nm-CMOS process is suitable for high-frequency RF circuits and high-speed digital circuits required for 5G system.