esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Initial Laboratory Tank Testing  
1. Geographic Location: Indoor controlled environment, National Taiwan Ocean University biological cultivation building.
2. Experiment Purpose: To verify fundamental system functions and calibrate motor control parameters prior to field deployment.
3. Experiment Content: The ROV was placed in a large aquaculture tank with a volume of approximately 1 cubic meter, filled with freshwater and live fish to simulate real aquaculture conditions. The ROV was equipped with sensors to measure temperature, pH, and dissolved oxygen levels, with cross-verification against handheld instruments to ensure accuracy. The communication system transmitted video via 4G mobile networks, allowing the research team to remotely control the ROV from a computer using the Smart-Fish Control System to evaluate data collection capabilities.
4. The test results indicated that in the initial testing environment, the underwater ROV was able to accurately execute a variety of actions, including motor speed tests, forward and backward movement, left and right turns, and descent and ascent.  
In the initial testing phase, the system demonstrated the effectiveness of multiple remote control functions. Researchers successfully operated the ROV using various control terminals, including smartphones, tablets, smartwatches, and multi-site central control centers, with seamless and smooth transitions between platforms. Figure 7 illustrates the smart device-controlled ROV monitoring capabilities, showing both smartphone and smartwatch interfaces used in laboratory and field testing environments. Through the web interface of the Smart-Fish Control System, all control commands were accurately transmitted to the ROV, enabling it to execute actions according to preset motion modes, with control delay times maintained at an average of 1 to 2 s. Notably, this network-based remote control method allowed personnel at the National Taiwan Ocean University control center to obtain and analyze water quality data and fish activity in the aquaculture tank in real-time, significantly enhancing monitoring efficiency.
###### Abstract  
Drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SF), an electromagnetic signal, has been proven to be an efficient tool for monitoring and assessing gross primary productivity (GPP) and drought. To address the issue of the sparse resolution of satellite-based SIF, researchers have developed different downscaling algorithms. Recently, the most frequently used SIF products had a spatial resolution of 0.05 degrees. However, these spatial resolution SIF data are not conducive to regional agricultural drought monitoring. In this study, we utilized the global 'OCO-2' solar-induced fluorescence (GOSIF) products along with normalized difference vegetation index (NDVI) and land surface temperature (LST) products. With the powerful advantages offered by Google Earth Engine (GEE), we could conveniently acquire the necessary data. Additionally, employing the random forest (RF) method, we successfully acquired downscaled SIF data at an enhanced spatial resolution of 1 km. Using those downscaled SIF results with 1 km resolution, an SIF anomaly index was established and calculated to monitor drought. Results showed that the RF-based downscaled SIF result followed the same trend as the GOSIF value. Subsequently, correlation coefficients between SIF and GPP were calculated. The downscaled SIF demonstrated a higher correlation with GPP from MODIS compared to 0.05-degree GOSIF, with coefficients of 0.74 and 0.68 in May 2018, respectively. Moreover, the SIF anomaly index showed positive correlations with crop yield; the correlation coefficients were 0.93 for wheat and 0.89 for maize. The drought index had a negative correlation with areas affected by drought, with a correlation coefficient of \(-0.58\). Finally, the SIF anomaly index was used to monitor drought from 2001 to 2020 in Henan Province. The 1 km SIF results obtained through the RF-based downscaled method were deemed reliable, thereby establishing the suitability of the SIF anomaly index for drought monitoring at a regional scale.  
solar-induced chlorophyll fluorescence; drought; random forest; downscaling 2024
## 2 Study Area and Datasets  
### Study Area  
The CONUS is located in the south of the North American continent, facing the Atlantic Ocean in the east, the Pacific Ocean in the west, Mexico and the Gulf of Mexico in the south, and Canada in the north, and is surrounded by the sea on three sides. The CONUS is divided into 18 watersheds by the United States Geological Survey (USGS), which are shown in Figure 2a and listed by 2-digit hydrological unit codes (HUC-2) [36] in Table 1. The CONUS is affected by complex terrain and climate change; its TWS variations differ significantly over regions, e.g., a severe drought from 2012 to 2015, heavy precipitation from 2016 to 2017 in California [28], and hurricane Harvey in August 2017, which induced heavy precipitation and water gain along the Gulf Coast [33].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HUC-2** & **Watershed** & **Area (km2)** & \(n\) \\ \hline
1 & New England & 171,083 & 17 \\
2 & Mid Atlantic & 301,894 & 26 \\
3 & South Atlantic-Gulf & 713,806 & 104 \\
4 & Great Lakes & 466,972 & 138 \\
5 & Ohio & 428,693 & 80 \\
6 & Tennessee & 107,319 & 27 \\
7 & Upper Mississippi & 491,732 & 139 \\
8 & Lower Mississippi & 262,302 & 28 \\
9 & Souris–Red–Rairy & 154,068 & 23 \\
10 & Missouri & 1,346,773 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Watershed division of the CONUS and the area and number of GNSS stations in the 18 watersheds and the CONUS.  
Figure 1: Illustration of the inversion of TWS of a specific pixel under Laplacian and spatial constraints. (**a**) represents the simple Laplacian constraints. (**b**) represents the complex Laplacian constraints. (**c**) represents spatial constraints.
## 3 Simulation Results and Discussion  
### Analytical Study of Normal Incidence Sound Absorption Coefficient of the HPSET with a Melamine Foam  
Figure 2 illustrates the analytical results of the normal incidence sound absorption coefficient of the HPSET with a melamine foam obtained from Equation (12), in which the sound absorption of HPSET and a single layer melamine foam are also shown for a comparison. In the following simulations, the inner diameter of the HPSET is \(2R=65\) mm, the number of perforations is \(N=2\), the diameters and lengths of the extended tubes are \(d_{0}=4.9\) mm and \(t=10\) mm, respectively. The Helmholtz resonance absorption peak of the HPSET is found at 380 Hz and the anti-resonance frequency of this combined absorber is observed at 500 Hz, while the quarter wavelength resonance frequency of the porous material is around 1500 Hz. It is shown in Figure 2a that the sound absorption of the composite absorber is superior to the single layer melamine foam and HPSET. It is clear that the sound absorption of a single layer porous foam is less than 0.5 below 500 Hz. While the composite absorber reaches a wideband sound absorption (greater than 0.5) in the frequency range from 350 Hz to 2000 Hz, hence combination of HPSET with melamine foam could greatly enhance the low frequency sound absorptive performance of the melamine foam.  
It is revealed from Figure 2b that the relative resistance of single layer melamine foam and HPSET are less than 1, which is less than the combined absorber in the frequency range from 360 Hz to 2000 Hz. Hence, the present absorber could improve the acoustic resistance and enhance the sound absorption. Meanwhile, the relative acoustic reactance of this combination is nearly zero at the resonance frequency of 380 Hz and 1500 Hz, which ensures the wideband absorptive performance of the proposed absorber. The low frequency sound absorptive performance could be further enhanced by combining multiple HPSETs with different resonance frequency.
## 3 Motivation and Problem Statement
### Problem Statement  
The previous versions of UML leave the case shown in Figure 1 as a semantic variation point and do not define which providers will receive the request. The current version of UML [2] mentions that semantics is the same when we use three binary connectors or a single quaternary connector. It also indicates that the instance that will handle the request is determined at execution time. As a result, UML still does not provide a method to define the routing of requests for this case.  
Figure 2 shows a part of the abstract syntax for the connectors defined in the UML. According to the definition, the _Behavior_ classifier is associated with the _Connector_ classifier using the _contract_ role. Note that the _contract role of a connector_ is distinct from the _contract of a port_ specified by the required and provided interfaces of the port. As an explanation for the contract role, UML indicates that _behaviors may be associated with connectors as contracts to specify valid interaction points across the connector_. However, the kind of behaviors that can be associated with connectors is not defined. Conversely, for other types of behaviors, such as _ProtocolStateMachines_, the exact purpose is documented, constraints are determined, and the application of the concept is demonstrated with concrete examples [2].  
We propose using the behaviors indicated by the contract role to define the coordination behavior. As a result, the complex coordination logic implemented inside the connected elements can be taken outside and implemented as behaviors of connectors. Separating the coordination concern from other responsibilities of the elements can increase their reusability. Behaviors associated with connectors also help resolve the ambiguities which may arise when we use n-ary connectors to connect more than two connectable elements. Finally, we can reuse the connector behavior specifications across scenarios that involve similar coordination requirements.  
Figure 1: Single requester connected to multiple providers.  
Figure 2: Abstract syntax in UML showing the contract role.
# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 4 Results
### Performance Metrics  
Four evaluation metrics are used in this paper to evaluate the performance of the classification model: _Accuracy_, _Recall_, _Precision_, and _F1-score_.  
The calculation formulas are shown in Formulas (16)-(19). _TP_ (True Positive): refers to the number of positive samples that are correctly classified as positive by the model; _FP_ (False Positive): refers to the number of negative samples that are incorrectly classified as positive by the model; _TN_ (True Negative): refers to the number of negative samples that are correctly classified as negative by the model; _FN_ (False Negative): refers to the number of positive samples that are incorrectly classified as negative by the model.  
(1) _Accuracy_  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{16}\]  
(2) _Precision_  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
(3) _Recall_  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
(4) _F1-score_  
\[F1\text{-}score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{19}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Batch Size & 128 \\ Maximum Sequence Length & 128 \\ Learning Rate & 0.01 \\ Dropout Rate & 0.2 \\ Multiple Attention Heads & 4 \\ Epoch Number & 50 \\ Hidden Size & 256 \\ Embedding Size & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameter settings of the experiment.
# Current State of the Gold Mining Waste from the Ores of the Ursk Deposit (Western Siberia, Russia)
## 3 Results
### Hydrochemical Composition of Surface Watercourses in the Ursk Drainage System  
An underground acidic sulfate solution with a pH value of 3.61 flows out from the dump (Table 2). It is characterized by a high concentration of sulfate ions (4500 mg/L) and specific electrical conductivity (EC) of 16 mS/cm. The major elements are typical macro-elements, i.e., the bedrock-forming components of polymetallic ores: Na, Mg, Al, K, and Ca. In addition, the Ursk drainage shows extremely high concentrations in Fe (970 mg/L) and high concentrations in Mn (19 mg/L) and Zn (14 mg/L) (Table 3).  
Figure 11: Digital model of the Ursk dump surface temperature.  
Figure 10: Fragment of the orthophotomap and digital elevation model.  
A distinctive feature of the drainage is the presence of a wide range of chemical elements, including ore-associated metals (Cu, Zn, Cd, Pb, Ba, Ni, Co, and Sb) and elements of the first class of danger (As, Hg, and Be) (Table 3).  
At a distance of 200 m from the dump EC drops to 2500 \(\mu\)S/cm (point 3/20), sulfate concentrations decrease more than fourfold, the pH value rises to 4.24, and concentrations of other macrocomponents and trace elements in drainage solution significantly drop (Tables 2 and 3).  
Figure 12 shows a fragment of a digital relief model obtained for the first time using aerial photography on the basis of the results of field work in 2020, which we presented as a model of water transport of chemical compounds from an acid-producing sulfide waste dump. Such studies make it possible to assess on a scale the changes in relief that occur over time in the territory adjacent to the mine tailings.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Distance, m**} & \multirow{2}{*}{**EC, \(\mu\)S/cm**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**SO\({}_{4}\)\({}^{2-}\), mg/L**} & \multicolumn{6}{c}{\(\mu\)g/L} \\ \cline{5-10}  & & & & & & **Na** & **Mg** & **Al** & **K** & **Ca** & **Fe** \\ \hline
1/20 & 0 & 16,000 & 3.61 & 4500 & 24 & 140 & 350 & 1.4 & 230 & 970 \\
2/20 & 50 & 1200 & 3.82 & 1450 & 19 & 35 & 20 & 0.94 & 100 & 12 \\
3/20 & 200 & 2500 & 3.55 & 1100 & 23 & 49 & 43 & 0.43 & 81 & 420 \\
4/20 &Figure 12: Schematic diagram of the structure of the Ursk drainage system: the distribution of oxidation, leaching, and mixing zones.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Height, m**} & \multirow{2}{*}{**Distance, m**} & \multicolumn{6}{c}{**mg/L**} & \multicolumn{6}{c}{**mg/L**} \\ \cline{3-14}  & & & **Mn** & **Zn** & **Cu** & **Cd** & **Pb** & **Ba** & **Ni** & **Co** & **Be** & **As** & **Sb** & **Hg** \\ \hline
0/20 & 316 & 0 & 19 & 14 & 3000 & 9.1 & 1200 & 83 & 290 & 260 & 3.5 & 1300 & 9.2 & 34 \\
2/20 & 320 & 50 & 2.5 & 0.83 & 1
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Attenuation Rate
#### 3.3.3 Derivation of \(I^{\prime}_{\theta}\) and \(I_{0}\)  
The acoustic intensity \(I^{\prime}_{\theta}\) inside the wind tunnel is defined, as follows:  
\[I^{\prime}_{\theta}=\frac{P_{e}^{2}}{\rho c}\cdot(1-M\cos\theta^{\prime})^{2}. \tag{22}\]  
On the other hand, the acoustic intensity \(I_{0}\) outside the wind tunnel is defined as follows:\[I_{0}=\frac{P_{b}^{2}}{\rho c}, \tag{23}\]  
where \(P_{b}\), \(P_{e}\), and \(\rho\) are the acoustic pressure at the point \(b\), the acoustic pressure at the point \(e\), and the density, respectively.
## 5 Structural Hybrid of Reviewed Converters
### Multilevel Bidirectional Resonant Converters
High-performance inverters, such as neutral-point-clamped and three-level inverters, can be interfaced with the proposed converter due to their three-level power conversion structure. * The proposed converter can be used for both single-phase and three-phase grid-connected applications as a bidirectional \(DC\)-\(DC\) converter. * The proposed converter is intended to be a good fit for the interface between the battery and the grid-connected inverter, allowing for high-efficiency electrical power exchange and energy conversion management. An isolated multilevel \(DC\)/\(DC\) converter for a power cell in a solid-state transformer (\(SST\)) that converts medium-voltage (\(MV\)) AC input to low-voltage (\(LV\)) or medium-voltage DC output has been studied. The \(AC\)/\(DC\) stage uses the three-level diode neutral-point-clamped (\(DNPC\)) types to take a higher voltage at \(AC\) input terminals. The high frequency and high efficiency design needs to be met with soft switching in the whole operating range. To achieve this aim, the \(LLC\) resonant topology is utilized in this configuration for isolated power conversion, owing to its high efficiency and high power density. The (\(DNPC\)) is first considered as the primary side topology for an \(LLC\) resonant circuit since it is the same structure as the phase legs for the \(AC\)/\(DC\) stage. The \(LLC\) circuit with a three-level \(DNPC\) as the primary side and two-level full bridge as the secondary side is shown in Figure 37. The transformer in the \(DC\)/\(DC\) stage needs to provide medium voltage isolation at a high power rating [155].
## 5 Power Generation Performance Loss Factor of the BIPV System
### DC-AC Conversion Loss
The PCS DC-AC conversion efficiency was lowered sharply by significantly lowering the input power below the rated power of the PCS, and the reduced PCS conversion efficiency had a critical impact on the system performance. Figure 13 shows the analysis results of the annual PCS conversion efficiency of each PV array. SI 30\({}^{\circ}\) showed the highest conversion efficiency (84.7%), followed in order by HR 0\({}^{\circ}\) (83.6%), SV 90\({}^{\circ}\) (81.3%), and WV 90\({}^{\circ}\) (80.3%). The analyzed PCS conversion efficiency, ranging from 80.3(%) to 84.7(%), was significantly lower than the indicated efficiency of the PCS products (94.5%). Moreover, they were also much lower than the PCS conversion efficiency range (89-91%) of PV systems derived in previous research [31]. If the input power of the PCS is within the range that allows the PCS to operate normally, the PR of the BIPV system used in this study would be expected to rise. Figure 14 shows the PR of each array calculated assuming that the annual PCS conversion efficiency was 90%. When the PCS conversion efficiency was assumed to be 90%, the ratio of the system loss decreased, causing an increase in the final yield, which eventually resulted in an increase in PR. The actual PR of each PV array in the BIPV system did not exceed 70%, but when the PCS conversion efficiency was assumed to be 90%, the calculated PR ranged from 73.1(%) to 75.0(%). The increase rate of the PR was calculated to be 5.4% for HR 0\({}^{\circ}\), 4.1% for SI 30\({}^{\circ}\), 7.2% for SV 90\({}^{\circ}\), and 8.0% for WV 90\({}^{\circ}\). These results showed that designing a BIPV system without considering the impact of the PCS or PV array on the other subsystem could reduce the overall performance by up to 8.0%.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 3 Methodology  
The complete methodology is given in Figure 3. The process starts with signal acquisition and processing and then tracks the satellites to estimate the Position, Velocity and Time (PVT) solution. At the stage of satellite tracking, some specified parameters are obtained that will be fed to RSQM for signal quality monitoring. Each stage is discussed in detail in the following sections.
# Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
## 3 Methods
### Defining Hand Behavior and Measurement Elements  
In this study, the target measurement behavior was hand rotation. To capture the rotation movements, we needed to define how they could be captured and interpreted imagistically. From the camera's view, if a person first shows his/her palm to the camera, one rotation consists of flipping his/her hands to show the back of his/her hands and then flipping them back to show his/her palms. More specifically, the axis of hand rotation can be a normal vector of the x-y-plane passing through the wrist, and the rotating body can be a position vector from the wrist to the tip of the thumb. This simple hand rotation is shown in Figure 2. It is an exemplary hand rotation with a rotation of 1 and a rotation angle of 360\({}^{\circ}\)captured through 30 frames. The three stages of hand rotation are increase, keeping, and decrease, and depend on the state of rotation angle change. The total rotational momentum is the sum of the increase in the rotation angle of the rotating body and the decrease in the rotating angle of the rotating body, and the unit is degrees. We used these to calculate the angle of rotation as the sum of the change angles of the increasing state and the decreasing state, and the time of rotation is the sum of the number of frames of all states. Because the input video was unified at 30 FPS, each frame was approximately 0.03 s.  
Figure 1: Process of our method.  
Figure 2: Example of hand rotation for 360\({}^{\circ}\)over 30 frames.
# On the Competitiveness of Oblivious Routing:
## 4 New Performance Metrics for Static Routing
### Evaluating the PoC and the EVoC  
Next, we discuss how to evaluate the above formulas numerically in the case when \(S\) is an arbitrary static routing function. First, we write (2) as a conditional probability:\(\eta(S)=\Pr(\kappa_{S}(\theta)>1)=\int_{\Theta}\Pr(\kappa_{S}(\theta)>1|\theta) \Pr(\theta)d\theta\). Observing that \(\Pr(\kappa_{S}(\theta)>1|\theta)=0\) if \(\theta\in R(\mathcal{S})\) and \(1\) otherwise, for the probability of congestion we find:  
\[\eta(\mathcal{S})=\int_{\Theta\setminus R(\mathcal{S})}\frac{1}{\mathrm{Vol}( \Theta)}d\theta=\frac{\mathrm{Vol}(\Theta\setminus R(\mathcal{S}))}{\mathrm{ Vol}(\Theta)}\;. \tag{4}\]  
Obtaining a closed form for the expected value of congestion needs a little more work. Writing (3) as a conditional expected value, we find:  
\[\mu(\mathcal{S})=\int_{\Theta}\kappa_{\mathcal{S}}(\theta)\Pr(\theta)d\theta= \frac{1}{\mathrm{Vol}(\Theta)}\int_{\Theta}\kappa_{\mathcal{S}}(\theta)d\theta\;. \tag{5}\]  
To evaluate (5), we need to evaluate the function \(\kappa_{\mathcal{S}}(\theta)\) over all \(\theta\in\Theta\). This amounts to solving the multi-parametric program \(\kappa_{\mathcal{S}}(\theta)=\min_{\lambda}\{\lambda:\mathcal{S}(\theta) \leq\lambda c\}\) as the function of the parameter \(\theta\). For a static routing function, this multi-parametric program is linear, therefore the value function \(\kappa_{\mathcal{S}}(\theta)\) is piecewise affine over \(\Theta\):  
\[\kappa_{\mathcal{S}}(\theta)=\{\kappa_{\mathcal{S}}^{i}(\theta),\Theta^{i}\} :\;i\in I\;,\]  
where \(I\) is a finite index set, \(\Theta^{i}:i\in I\) is a polyhedral complex on \(\Theta\), and \(\kappa_{\mathcal{S}}^{i}(\theta)=v^{i}\theta+w^{i}\) are affine functions over \(\Theta^{i}\)[44]. Triangulate each subregion \(\Theta^{i}\) and let the resultant simplices be \(R^{ij}:j=1,\ldots,I_{i}\). Denote the vertices of the simplex \(R^{ij}\) by \(q_{l}^{ij}:l=1,\ldots,K+1\). Then, using the Lasserre-Avrachenkov theorem [467 ]:  
\[\mu(\mathcal{S})=\sum_{i=1}^{l}\sum_{j=1}^{I_{i}}\left(w^{i}\,\mathrm{Vol}(R^{ ij})+\frac{\mathrm{Vol}(R^{ij})}{K+1}\sum_{l=1}^{K+1}v^{i}q_{l}^{ij}\right) \tag{6}\]