esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 2 Methodology
### Transformation  
After the skeletonization, the endpoints of the structure \(\mathrm{S}(\mathrm{Q}_{\mathrm{in}})\) were obtained. Endpoints are extremes of arcs and correspond to tips of pattern protrusions, which have only one connected pixel in the eight neighborhoods of the skeleton. In the target, the endpoints are the tips of the contour protrusions with the largest curvature in the local arc segment, which will not change due to the change of imaging mode. After obtaining the target skeleton endpoints of the infrared image and polarization image, the endpoints were used as control points for image matching in our method. With T as the transform matrix, the transformation can be expressed as  
\[\mathrm{Fixed\ image}\cdot\mathrm{T}\ =\ \mathrm{moved\ image}, \tag{6}\]  
therefore, we used  
\[\mathrm{T}_{\mathrm{inv}}=\ \mathrm{fixed\ points\ matrix}/\mathrm{moved\ points\ matrix}, \tag{7}\]  
and then,  
\[\mathrm{Registered\ image}\ =\ \mathrm{moved\ image}\cdot\mathrm{T}_{ \mathrm{inv}}, \tag{8}\]  
to give the registered images.
# Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets
## 3 Optimizing the Hyperparameters of the Classifiers
### Support Vector Machines  
The traditional (non-deep) strategy that we select for our classification is Support Vector Machines (SVMs). SVMs are supervised learning models that build a deterministic linear classifier. They are based on finding a separating hyperplane that maximizes the margin between the extreme training data of opposite classes. New examples are then mapped into that same space and predicted to belong to a class, on the basis of which side of the hyperplane they fall on. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, by implicitly mapping their inputs to a higher dimensional space, see also [22,28]. The hyperparameters tested for SVMs are:Kernel: the type of kernel used. For both the optimization approach and the grid search approaches we use (Linear; Radial Basis Function (RBF)). In the case of RBF, it is important to define the penalty parameter \(c\) of the error term and the kernel coefficient \(\gamma\).
* Coefficient \(\gamma\): is the inverse of the standard deviation of the radial basis kernel, and can be seen as the inverse of the radius of influence of samples selected by the model as support vectors. For the optimization approach and for the extended grid search we use \(\gamma=(2^{-19},2^{-18.7},\ldots,2^{20})\) with step \(2^{i+0.3}\) for a total of 131 values. For the grid search we use \(\gamma=(2^{-19},\ldots,2^{20})\) but step \(2^{i+1}\), for a total of 40 values.
* Penalty \(c\): is the regularization parameter of the error term. This value allows one to trade off training error vs. model complexity. For the optimization approach and for the extended grid search we use \(C=(2^{-8},2^{-7.7},\ldots,2^{23})\) with step \(2^{i+0.3}\) for a total of 95 values. For the grid search we use \(C=(2^{-8},\ldots,2^{23})\) with step \(2^{i+1}\) for a total of 32 values.
* Class_weights: specifies how the errors in the different classes are weighed during the training. The possible options are: (1) weights proportional to the class frequencies; (2) weights inversely proportional to the class frequencies; (3) provides uniform class weights, an error that has the same importance for any class. For both the optimization approach and the grid search approaches we use all possibilities (1, 2, and 3).
## 4 Discussion
Drought episodes usually start with rainfall deficiency, which leads to reduced soil moisture and it is often assumed that ET decreases when soil moisture decreases. On the other hand, it has been discussed that ET is restricted to low values of available soil moisture [94,95]. Therefore, for different soil moisture content, ET will change based on the variability in atmospheric conditions rather than the variability of soil moisture. In humid climates which are generally energy limited, during drought, atmospheric conditions intensify ET and lead to increased ET [95]. Similarly, in water limited dry  
Figure 8: The distribution of relative anomaly of evapotranspiration (ET), GPP and WUE for the 2002, 2008, 2011 and 2012 drought episodes. In each plot, the blue curves on the axes show the distribution of the corresponding variable. Figure 9: Spatial distribution of ET anomalies (mm) for the 2002, 2008, 2011 and 2012 drought episodes. climates, positive ET anomalies have been observed during warm conditions that often coincide with drought [40; 93; 94; 96]. The deep and extensive root systems of plants can obtain water from lower levels of soil column near the water table rather than the smaller top layers of soil zone, which may increase the transpiration and consequently increase ET during drought [40; 93]. Drought can cause a wide range of impacts on terrestrial ecosystems, including influence the vegetation physiology, phenology, growth, pest outbreak and fire occurrence [1; 97]. In this study, we evaluated drought impacts focusing on the changes of soil moisture and ecosystem water use efficiency during the 15 years of 2002-2014 over the CONUS. Using fine spatial and temporal resolution remotely sensed data and model simulations, we were able to quantify the impacts with high resolution and significant meaningful patterns were extracted. The results of this study confirmed certain previous findings, such as increase in WUE and significant decrease in GPP and ET during severe drought. This study showed that the increase rate of WUE is directly related to drought severity and for a severe or extreme drought episode, there is a high likelihood of increasing WUE. Moreover, this study investigated terrestrial drought recovery based on GPP variations and concluded that drought recovery is positively correlated with drought severity and drought duration. These positive correlations shed light on the understanding of the response of terrestrial ecosystems to drought and their importance for forecasting ecosystem dynamics in future climate scenarios. The findings of this study highlighted the substantial impacts that drought can have on the ecosystem, which implies the necessity of advancing drought forecasting methods and incorporating proactive drought risk management strategies in order to mitigate such environmental risks. This study investigated the recovery duration over the CONUS after agricultural drought using GPP, which has never been investigated. Additionally, results showed that WUE increases in the regions that experienced severe or worse drought condition.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
###### Abstract  
DNN-based Speech Enhancement (SE) models suffer from significant performance degradation in real recordings due to the mismatch between the synthetic datasets employed for training and real test sets. To solve this problem, we propose a new Generative Adversarial Network framework for Noise Modeling (NM-GAN) that creates realistic paired training sets by imitating real noise distribution. The proposed framework combines a novel 7-layer U-Net with two bidirectional long short-term memory (LSTM) layers that act as a generator to construct complex noise. NM-GAN generates enough recall (diversity) and precision (noise quality) in its samples through adversarial and alternate training, effectively simulating real noise, which is then utilized to compose realistic paired training sets. Extensive experiments employing various qualitative and quantitative evaluation metrics verify the effectiveness of the generated noise samples and training sets, demonstrating our framework's capabilities.  
Noise Modeling; training set; Generative Adversarial Network; speech enhancement
## 2 Methods
### Observing and Measuring Fine-Roots  
In May 2018, we installed three transparent tubes of cellulose acetate butyrate near each selected tree. The tubes, with a length and a diameter of 60 cm and 5.2 cm, respectively, were installed in one line at distances of one, one-and-a-half, and two meters from the trees at an angle of \(\pm 30^{\circ}\) to the soil surface. As such, the tubes reached a vertical viewing depth of \(\pm 35\) cm [4]. Because the installation of these tubes disturbed the soil and fine-root production, we waited until April 2019 for the first acquisition of images [98; 99]. During the stabilization period, four tubes were damaged. New tubes were installed allowing less time for the soil and fine-root production near these tubes to reach equilibrium again. During the period of May 2018 to March 2021, all tubes were sealed using a rubber cork lid and covered by an empty plastic bottle cut in half. Nevertheless, some tubes proved to be not completely waterproof. Therefore, before each sampling session, we removed moisture in the tubes using a sponge and a hand siphon pump.  
From April 2019 to March 2021, we collected images of the fine-roots using the manual BTC-2 minirhizotron camera and the associated I-CAP software (Bartz Technology Corp., Carpinteria, CA, USA). Thanks to a small hole in the upper side of the tubes and the smucker handle of the minirhizotron, a stable and consequent positioning of the minirhizotron on the tubes was possible. We were able to acquire for each month and from each tube a series of 22 images taken from the same downwards viewpoint, allowing repetitive and non-destructive measurements of the same fine-roots over time. Each image represented a soil area with a width of 13 mm and a length of 9 mm. Finally, all images were analyzed using the software WinRHIZO Tron MF 2015b (Regent Instruments Inc., Quebec, Canada), resulting in temporal data on the number, birth, death, length, surface area, average diameter, and volume of the fine-roots. We note here that the dates of the birth and the death of the fine-roots were the dates of the sampling session at which the fine-roots were first or last observed, respectively.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### The DO User Data Rate Loss as Goal Cost  
Let us recall that our aim is to assess performance in a scenario in which a GO system coexists with a DO system. In the considered scenario, the DO user, \(UE_{d}\), uploads data  
Figure 2: Validation of Assumptions 1 and 2.  
(e.g., a video stream) with the aim of maximizing its data rate. However, the system is cooperative, and the DO user aims to maximize its data rate without preventing the GO user from attaining the desired goal effectiveness. Denoting by \(\mathbf{w}_{d}\in\mathbb{C}^{M_{d}\times 1}\) the combining vector of \(AP_{d}\), the received SINR at \(AP_{d}\) at time \(t\) reads as follows:  
\[\text{SINR}_{d,t}=\frac{|\mathbf{w}_{d,t}^{H}\mathbf{h}_{dd,t}|^{2}P_{d,t}}{N_{ 0}W+|\mathbf{w}_{d,t}^{H}\mathbf{h}_{dg,t}|^{2}P_{g,t}}, \tag{16}\]  
and the data rate can be approximated by the Shannon formula, as we do not need to assess the PER performance for the DO system (however, generalizing this would be straightforward):  
\[R_{d,t}=W\log_{2}(1+\text{SINR}_{d,t}). \tag{17}\]  
Denoting by \(\mathcal{R}_{d,\text{max}}\) the maximum achievable average data rate by the DO system in the absence of the GO one and with transmission power \(P_{d}=P_{d,\text{max}}\), where \(P_{d,\text{max}}\) stands for the maximum transmission power of \(UE_{d}\), the goal cost can be defined as the _average relative \(UE_{d}\) data rate loss_, i.e., it can be written as follows (cf. (1)):  
\[\mathcal{C}=\frac{\mathcal{R}_{d,\text{max}}-\mathcal{R}_{d}}{\mathcal{R}_{d, \text{max}}}. \tag{18}\]  
The lower the DO user data rate is, the higher the goal cost is. In other words, the price to make the GO user achieve its goal with target goal effectiveness is paid by the DO user through its achievable uplink data rate. In the sequel, we assume that the DO user always has backlogged traffic, i.e., it continuously transmits and interferes with the GO system. This can be easily generalized.  
Now, we have all elements describing goal effectiveness and goal cost in the investigated scenario. In the next section, to gain insights on relevant parameters to be controlled and optimized in the proposed system, we will first present a performance assessment, obtained through Monte Carlo simulations. Then, based on the acquired messages, a goal-oriented problem formulation, along with its solution, will be presented in Section 4. We believe that this gentle introduction will help the reader understand all ingredients of the system, and also the reasoning behind the goal-oriented approach and the corresponding proposed optimization problem.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 5 Experimental Validation
### Experiments on UR5e Robot
#### 5.2.2 Optimal Parameter Computation
For instance, the duration of the unoptimized trajectory scaling1 is identical to that of the optimized trajectory with C1, so its time parameters are calculated as follows: \(t_{1}=T_{a}=0.271\times 1.205/1.117=0.292\;\mathrm{s}\), \(T_{v}=0.575\times 1.205/1.117=0.621\;\mathrm{s}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trajectory** & & \(T_{a}\) **(s)** & \(T_{v}\) **(s)** & \(T_{tot}\) **(s)** & \(v\) **(m/s)** & \(a\) **(m/s)** \\ \hline \multirow{3}{*}{Unoptimized trapezoidal velocity} & Original & 0.271 & 0.575 & 1.117 & 0.65 & 2.4 \\  & Scaling1 & 0.292 & 0.621 & 1.205 & 0.603 & 2.064 \\ \cline{1-1}  & Scaling2 & 0.283 & 0.601 & 1.167 & 0.622 & 2.199 \\ \cline{1-1}  & Scaling3 & 0.304 & 0.646 & 1.255 & 0.579 & 1.903 \\ \hline \multirow{3}{*}{Optimized trapezoidal velocity} & C1 & 0.388 & 0.488 & 1.205 & 0.65 & 1.814 \\ \cline{1-1}  & C2 & 0.271 & 0.625 & 1.167 & 0.614 & 2.267 \\ \cline{1-1}  & C1, C2 & 0.358 & 0.538 & 1.255 & 0.614 & 1.713 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computed trajectory parameters for experiment 1.
## 3 Results and Discussion
### Data Preparation  
To optimize the IFI and predict the frictional properties of HFST samples, microtexture and macrotexture measurements were obtained using DFT and CTM devices for various aggregates with different gradations. Figure 7 presents the multivariate scatter plot of the collected data from DFT and CTM tests on all samples with different aggregates and sizes. To make it easy to identify variable distributions, facilitate the creation of precise prediction models, and visually depict connections between values, data visualization is essential. Across all aggregate sizes, CB consistently showed the highest COF in comparison to Rhy, as seen by the multivariable scatter plot and pair plot. This pattern demonstrates CB's better frictional performance in HFST. The Pearson correlation coefficient was calculated as r = \(-\)0.071 with a \(p\)-value of 0.7406, indicating a very weak and statistically insignificant linear relationship between these two variables in the dataset. This suggests that COF and MPD may contribute independently to the IFI and supports the rationale for including both variables in multivariate nonlinear modeling.  
Because of its unique morphological characteristics, such as texture, angularity, and particle shape, CB HFST showed the greatest COF among the various sizes. These features improve surface roughness and interlock, which improves skid resistance. According to earlier research, CB's microtexture and macrotexture are closely related to its exceptional performance and are essential for maintaining high friction levels even after lengthy polishing cycles. The pair plot of the data gathered from the DFT and CTM tests on all samples with various aggregation sizes and types is shown in Figure 8.  
The distribution of each parameter is shown by the diagonal components, and CB has a tendency toward higher COF values, suggesting superior frictional performance in comparison to Rhyolite. The Outlier analysis was conducted using interquartile range (IQR)-based filtering for COF and MPD across polishing cycles. The analysis revealed zero potential outliers.  
The scatter plots in the lower left and upper right corners do not show a strong linear correlation between COF and MPD, emphasizing that higher texture depth does not necessarily equate to higher friction.  
Figure 6: MPD variation across polishing cycles for different aggregate types and sizes.
## 4 Simulation and Results
### Performance Metrics  
Items were categorized into four types, namely 1, 2, 3, and 4. Each type had an equal probability of occurring, with a 1/4 chance of a particular item being type i (where i = 1, 2, 3, or 4). Additionally, the type of a specific work piece was not influenced by the types of its preceding or succeeding work pieces. Both Board Assemblies 1 and 2 and Routers 1-3 could process all types of jobs, but the individual processing time for each type varied, as presented in Table 1. Router1. If the type of a particular job differed from the type of the previous job, then Router 3 had a setup time of 20 s, with the following factors considered:  
- Average production per type;  
- Average stocker load;  
- Average buffer latency per type.  
Table 1 shows the four types, board assembly, and router processing time, while the following factors were considered:  
- Router set-up time: 20 s;  
- Item generation: Type 1-4 with uniform probability;  
- Run time: 10 days (warm-up time of 3 days);  
- RL (PPO algorithm);  
- Time step (number of training times): 10,000 times;  
- Reward definition, illustrated below.  
The formula is as follows: (router's average process time)/(Item Sink 1 entry time \(-\) item stocker exit time). To implement the model shown in Figure 17, which can be used to direct items entering the stocker to the optimal object among Routers 1-3, the reinforcement learning components are as follows: the state St = [Router1 Type-1, Router2 Type-1, Router3 Type-1] In the state expression, from Router 1 through Router 3, Type-t \(-\) 1 refers to the type number of the (t \(-\) 1) job processed by Routers 1-3 at the current time t. The reward can be calculated using the formula R = K/[Tt(Sink 1 entry) \(-\) Tt(stocker exit)], where K is a constant value and Tt represents the total time.  
The behavior of the agent in RL involves selecting a specific item number from Routers 1-3. The constant K in the agent's reward is 22, which represents the average processing time for each type of item across Routers 1-3. Here, Tt(E) refers to the time at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Board Assembly** & **Router** \\ \hline Type 1 & 20 & 19 \\ Type 2 & 17 & 25 \\ Type 3 & 14 & 23 \\ Type 4 & 15 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process time.  
Figure 17: Factory simulation system diagram.  
which event E occurs for the item. In the simulation with RL, we defined the variables of task assignment based on the process time of the production line. RL mainly determines the behavior of the agent in the state space, and the average waiting time was determined based on the average production. We defined the actions that the RL agent could perform within the simulation. By iteratively learning the lead time and average waiting time, it continuously checked the reward signal to find a solution that optimized the average waiting time.
## 2 G-R for HSRs  
### Architecture of Networks  
The architecture of networks in the 5G-R for HSRs mainly involves three parts: access network, bearer network, and core network, as shown in Figure 2. The smooth and stable mission-critical communication services are supported, such as signaling, onboard video surveillance, driver look-ahead as well as supporting passenger high-data-rate connectivity [29]. The building baseband unit (BBU) or the distributed unit (DU) offers large-scale cooperative processing and is able to achieve lots of real-time connectivity. The fronthaul network consists of gNodeBs and BBU/DU through a high-capacity optical fiber. The network decouples completely the control plane and the user plane, then reconstructs flexibly the system control. CloudRAN is introduced to address the requirements for the reconfiguration of the access network. If there is a mobile edge computing server, it is usually deployed in the same set of servers as the centralized unit (CU). In terms of the physical level, the bearer network is divided into fronthaul, middlehaul, and backhaul. The concept of the logic of middlehaul for the bearer network is proposed. The cellular structure is changed to the chain structure in a private communication system for HSRs because each gNodeB contains two sectors, while a public communication system typically consists of three sectors. The HST runs at an average speed of \(v\) and passes gNodeB\({}_{(i-1)}\) (\(i=1\), \(2\),..., \(N\)), gNodeB\({}_{i}\), and gNodeB\({}_{(i+1)}\) in turn.  
As shown in Figure 3, there are corresponding interfaces to connect each element in 5G-R for HSRs. NG is the interface between the access network and the core network. F1 is the interface between the functional entities of CU and DU, which supports the signaling exchange between endpoints and the data transmission of each endpoint. The air interface of 5G-R is Uu, which is the port between UE and gNodeB. In order to distinguish it from LTE-R, it is defined as NR-Uu.  
Figure 1: Principle of collecting massive amounts of measured data.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 2 Data and Preprocessing
### Remote Sensing Data  
The remote sensing data presented in Table 1 were utilized during the construction of the GPP estimation model. Among these, the GLASS downward shortwave radiation data played an important role in furnishing information concerning radiant energy, which serves as the primary energy input for photosynthesis [34]. The GLASS data were processed using information from multiple sensors and satellite platforms, including but not limited to various multispectral and hyperspectral sensors like MODIS, AVHRR (Advanced Very  
Figure 1: The distribution of vegetation types and AmeriFlux sites in the study area.  
High-Resolution Radiometer), and MERIS (Medium-Resolution Imaging Spectrometer). MCD43A4 is a Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance dataset and contains MODIS bands from 1 to 7. The MOD11A1 dataset contributes to surface temperature. Band 11 of the MODOCGA dataset can track the changes in xanthophyll during the photosynthesis process and serves as an input for the photochemical reflectance index [35]. Additionally, the MOD13Q1 dataset provides the Enhanced Vegetation Index (EVI), which reflects vegetation growth vigor. These data cover important factors affecting vegetation photosynthesis, collectively offering comprehensive information on vegetation growth and GPP variations.  
We associated each record from the site dataset with the corresponding remote sensing data. Every satellite dataset underwent a quality label filtration process and was later resampled to a 1 km resolution using the GEE, with the exception of the GLASS dataset. In addition to the central pixel of flux sites, the surrounding 24 pixels (approximately 5 km \(\times\) 5 km) were also considered for contrast with other GPP products that have coarser spatial resolutions.