esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 2 Branchy Neural Network with Attention Mechanism
### Classification Neural Network for Attention Allocation  
The classification neural network is designed as a three-layer fully connected network, which has \(m\) output neurons in the output layer to predict the NPP of \(m\) noise interference types. Its hidden layer has 2048 neurons with rectifier linear activation units (ReLU) to ensure sufficient model capacity for noise classification. Considering the multiple noises in different application scenarios, multi-classified softmax cross-entropy function is used as the loss function for DNN training:  
\[\textit{Class\_Loss}=-\frac{1}{m}\sum\limits_{i=1}^{m}T_{i}\log(\frac{e^{z_{i} }}{\sum\limits_{j=1}^{m}e^{z_{j}}}) \tag{1}\]  
where \(T_{i}\) represents the \(i\)-th ideal noise label for learning, \(m\) denotes the number of class categories, \(z\) denotes the output vector of the fully connected layer, and \(z_{j}\) represents the \(j\)-th value of vector \(z\). The SoftMax function in Equation (1) ensures that the sum of attention allocation is one.  
To further strengthen the noise classification accuracy of the classification network, a noise-aware feature is extracted for each input frame. Since the NPP is related to the posteriori signal-to-noise ratio (SNR) levels, a weighted average approach based on the hard threshold in log power spectrum (LPS) domain is proposed to update the noise spectrum feature of each frame, as shown in Equation (2):  
\[\begin{array}{l}if\ \frac{\gamma(k,t)}{N(k,t-1)}<\beta\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
gives a good compromise between underestimation and overestimation of the noise spectrum [30]. In addition, the concatenated input features are normalized to a mean value of zero and a variance of one, beneficial for gradient descent.  
After model training, the classification network produces the NPP of each noise type in its output layer. As shown in Figure 1, \(p_{n_{1}}\),..., \(p_{n_{m}}\) represent the presence probability of \(m\) noises, \(n_{m}\) is the index of different noise types, and the sum of these probability values is one. This means that the classification network analyzes and generates the proportion of noise components for each noisy input frame, and therefore controls the attention emphasis of the denoising network on different branches. These estimated NPP values are regarded as the prior environmental information for the training and testing of the denoising neural network.
## 1 Introduction
A more recent work [17] used NOAA-AVHRR satellite data and the GLO-PEM model for 1981-2000 and obtained much higher values between 3.56 (1983) and 4.57 PgC (1998). This recent work showed a positive trend of approximately 8.5% increase per decade in NPP. A CASA model based study for the period 1981-2006 [18] finds that the NPP increased by about 8.5% during this 25-year period, primarily due to enhancement in agricultural productivity; the climate had a relatively small role (15%). In this paper, we investigate the trends and variability of AVHRR (Advanced Very High Resolution Radiometer)-derived NPP over India. Specifically, we focus on the linear trend and interannual seasonal variability in NPP for the period 1982-2006. Multivariate regression is used to understand the causes for the trends and interannual and seasonal variability in NPP. The NPP data that we use here is different from data that have been used in the past. Investigations using different datasets and methods help to identify the robustness and uncertainty in earlier results. For instance, we find trends similar to past studies, but our results on interannual variability linking NPP and ENSO are not in agreement with earlier studies.
## 2 System and Channel Models  
### System Model  
Here, a dual-hop half-duplex DF cooperative relaying system is considered, as shown in Figure 1, where a single antenna relay \(R\) assists a source \(S\) with multiple antennas \(N_{1}\) to communicate with a destination \(D\) with multiple antennas \(N_{2}\). It is assumed that the CSI is available at each node. The communication from \(S\) to \(D\) is possible only through node \(R\), and the whole transmission of a wireless signal is completed in two consecutive phases. Node \(S\) uses the maximum-ratio transmission (MRT) technique and propagates wireless signals to node \(R\) during the first phase. At node \(R\), the collected signals are first decoded and encoded, then forwarded to node \(D\) during the next phase. The maximal-ratio combining (MRC) technique is employed at node \(D\) to aggregate all the received signals from node \(R\). Let the channel vectors, \(\mathbf{h}_{1}\in\mathcal{C}^{N_{1}\times 1}\) and \(\mathbf{h}_{2}\in\mathcal{C}^{1\times N_{2}}\), be from \(S\) to \(R\) and from \(R\) to \(D\) links, respectively.  
The total end-to-end instantaneous SNR \(\gamma_{t}\) at node \(D\) is given by [45]:  
\[\gamma_{t}=\min\left(\gamma_{1},\gamma_{2}\right) \tag{1}\]  
where \(\gamma_{\ell}=||\mathbf{h}_{\ell}||/n_{o_{\ell}}\) (\(\ell=1\), 2) represents the \(\ell\)-th hop instantaneous SNR, with \(||\cdot||\) denoting the Frobenius norm, and \(n_{o_{1}}\) and \(n_{o_{2}}\) show the average power of the additive white Gaussian noises (AWGNs) at node \(R\) and at each antenna of node \(D\), respectively.  
Figure 1: Dual-hop DF relaying system with beamforming.
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 3 The Proposed Method
### The Improved 3D Gray-Level Co-Occurrence Matrix (IGLCM_3D)
#### 3.2.2 Computation of the Haralick Feature Vector  
The 3D GLCM is computed from the quantized image by considering the image intensities information of the input volumetric cube. The number of quantization levels is \(\text{N}_{\text{q}}=\ 32\), chosen as a trade-off between time efficiency and loss of data. The considered directions are all 13 independent directions provided in Table 2 and the considered distances are: 1 voxel and 2 voxels, respectively. The GLCM matrix is generated for all considered orientations and distances, resulting 26 matrices (13 directions \(\times\) 2 distances). All matrices are normalized. The computed GLCM does not constitute the feature vector, it is just a tool used to compute different statistical indicators. For the GLCM matrix, 14 Haralick indicators [27] are determined: energy, entropy, correlation, contrast, homogeneity, variance, sum average, sum entropy, sum variance, difference entropy, difference variance, information measures of correlation 1 and 2 and the maximal correlation coefficient. They are computed for each of the 26 obtained matrices. The Haralick feature vector (GLCM feature vector) is obtained by considering the block scheme from Figure 7.  
Figure 6: The IGLCM_3D block scheme (©© [2020] IEEE. Reprinted [adapted], with permission, from [29]). \({}^{1}\) Gradient Magnitude Co-Occurrence Matrix; \({}^{2}\) Azimuth Angle Co-Occurrence Matrix; \({}^{3}\) Elevation Angle Co-Occurrence Matrix.  
For each distance, there are 14 Haralick indicators calculated for each of the 13 directions. For each Haralick indicator, the average and standard deviation are computed for all directions in order to gain a degree of invariance to rotation. By concatenating the features obtained for the two considered distances, a degree of invariance to the observation scale is also obtained. The final Haralick feature vector contains 56 characteristics as detailed in the Appendix A.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
###### Abstract  
The Third Generation Partnership Project (3 GPP) Release 16 defines the sensing-based semi-persistent scheduling (SPS) as the resource allocation scheme for Sidelink Mode 2 in New Radio (NR)-based vehicle-to-everything (V2X) communication. A well-known issue in Mode 2 is the persistent packet collision that results from two or more vehicles repeatedly using the same resource for transmission. It may create serious safety problems when the vehicles are in a situation where only the broadcast safety beacons can assist in driving. To resolve this issue, a solution that relies on the feedback from neighboring vehicles is proposed, through which the vehicles suffering from persistent packet collisions can quickly part and select other resources. Extensive simulations show that the proposed broadcast feedback scheme reduces persistent packet collisions by an order of magnitude compared to SPS, and it is achieved without sacrificing the average packet reception ratio (PRR). Namely, it is the quality aspect (i.e., burstiness) of the packet collisions that the proposed scheme addresses rather than the quantity (i.e., total number of collision losses). By preventing extended packet loss events, the proposed scheme is expected to serve NR V2X better, which requires stringent QoS in terms of the information update delay thereby helping to reduce the chances of vehicle crashes.  
NR V2X; Sidelink Mode 2; semi-persistent scheduling (SPS); persistent packet collisions; in-band emission (IBE); broadcast feedback +
Footnote †: This paper is an extension of Persistent Scheduling with Broadcast Feedback for Cellular V2X Communication, originally presented at the IEEE Vehicular Technology Conference (VTC)-Spring, 2021.
## 4 Numerical Results and Analysis  
In this section, we validate the efficiency and robustness of the proposed optimization method under different UAV heights and environments. The optimized planned trajectory and time sequence of communication resources are also demonstrated, under our assumptions introduced in the model establishment, and trajectory prediction of moving ground users. Figure 5 demonstrates path planning for several scenarios.  
To train UAV planning, the trajectory of moving ground users needs specification at first. With the CA model mentioned above, we have obtained the predicted data of ground users' location along the time sequence successfully.  
For a simple demonstration and test, the number of ground users is set as 10, and the range of earthquake-stricken area is rectangular with length = 100 m and width = 50 m, with several obstacles scattered in, resembling residential buildings. The typical speed of a moving user in adequate wide space is 5 m/s, with evacuation time discretized into a time series with an interval of 1 s. All the residents have already left architecture at the first second, with random locations in the valid areas. As time passes, all users move in the shortest path directed to escape. In this simulation, the evacuation is completed in 80 s, thus we obtain the predicted trajectory of moving ground users. The software interface of the simulation is shown in Figure 6.  
Figure 5: **Trajectory of ground users predicted by CA model.** The moving trajectories of considered users are depicted using scatter plots in different colors, showing that they effectively avoided obstacles and found the shortest possible paths to the exit, in different tested scenarios. **(a)** Predicted trajectory of moving users with 2 obstacles of different sizes. **(b)** The predicted trajectory of moving users with 6 scattered obstacles. **(c)** Predicted trajectory of moving users with 1 centered obstacle.  
As the simulated results demonstrate, the trajectory of users can be predicated and the data can be applied to train ISATR later. We have generated different user trajectories to evaluate the adaptability of our proposed method, with scenarios varied in user distribution, obstacles, and exits.
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
## 2 Summary of Study Area and Data Sources
### Data Source  
The remote sensing imagery data used in this study are from the Chinese Energy and Water Balance Monitoring System (CEWBMS), which was jointly built by the National Meteorological Administration, the Institute of Geographic Sciences and Natural Resources Research, and the Chinese Academy of Sciences. Land surface remote sensing imagery is one of the standard products obtained from this system. This system uses the Japan GMS (geostationary meteorological satellite) as its data source [28]. The land surface temperature in this study is one of the CEWBMS' standard energy products, of which the spatial resolution is 5 km \(\times\) 5 km [29].  
This study uses data from 24 land surface temperature observation stations in the Beijing-Tianjin-Hebei region and their corresponding daily average surface temperatures, which were provided by the China Meteorological Science Data Sharing Centre [30]. We use the location coordinates of the 24 stations to retrieve the remote sensing-inverted daily land surface temperature data for the Beijing-Tianjin-Hebei region from 2002 for each station during that year. We introduce the daily average surface temperature of the observation stations into the Kalman filter as the \"true value\" to optimize the remote sensing inversion data. In addition, we obtain DEM elevation raster data with a resolution of 30 m from the USGS website, which are used to determine the elevations of the observation stations. We use the elevation data to determine a zoning strategy based on previous knowledge. Based on the characteristics of the standard products of the CEWBMS in combination with the land surface data for the study area and numerous experiments, this study selects the land surface temperature from 2002 as the study object to conduct time series reconstruction.
## 3 System Description
### Reference Coal-Fired Power Unit

Table 4: Parameters of the regenerative steam extraction system of CFPU. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Fuel**} & **Proximate Analysis (wt\%)** & \multicolumn{4}{c}{**Ultimate Analysis (wt\%)**} & \multicolumn{4}{c}{**LHV (kJ/kg)**} \\ \hline Coal & A & M & C & H & O & N & S & \\  & 19.70 & 6.00 & 67.60 & 2.70 & 1.80 & 0.90 & 1.30 & 24,720 \\ \hline \hline \end{tabular}  
* A: ash; M: moisture; LHV: lower heating value.  
\end{table} 
Table 5: Properties of the fuel used by CFPU (as received basis). calculation, the designed urea mass for the operation of CFPU should not be less than 1.21 t/h. The results obtained by theoretical calculations are described in Table 7.  
\[\Delta Q_{NO_{\chi}}=\frac{M_{NO}}{M_{NO_{2}}}(NO_{X,in}-NO_{X,out}) \tag{1}\]  
\[m_{NH_{3}}=\frac{n\times\Delta Q_{NO_{\chi}}\times V\times M_{NH_{3}}}{M_{NO} \times 10^{6}\times\eta} \tag{2}\]  
\[m_{CO(NH_{2})_{2}}=\frac{m_{NH_{3}}\times M_{CO(NH_{2})_{2}}}{2\times M_{NH_{3}}} \tag{3}\]  
where \(\Delta Q_{NO_{\chi}}\) is the amount of removed NO\({}_{\chi}\), mg/m\({}^{3}\); \(m_{NH_{3}}\) is the mass flow of NH\({}_{3}\), kg/h; \(m_{CO(NH_{2})_{2}}\) is the mass flow of urea, kg/h; n is the mole ratio of NH\({}_{3}\) to NO\({}_{\chi}\), taking 0.96; \(V\) is the inlet fuse gas volume of SCR denitration device, m\({}^{3}\)/h; \(M_{NO}\), \(M_{NO_{2}}\), \(M_{NH_{3}}\) and \(M_{CO(NH_{2})_{2}}\) are the molar masses of NO, NO\({}_{2}\), NH\({}_{3}\) and CO(NH\({}_{2}\))\({}_{2}\), respectively; \(\eta\) is the volume fraction of the NO in NO\({}_{\chi}\), taking 99% [39]. However, the heating steam source used during the operation of urea hydrolyzed is high-temperature superheated steam (the third stage extraction steam of steam turbines). The extraction steam is of high quality and is wasted seriously as an external heating source. It needs to be sprayed demineralized water to cool down and reduce pressure before entering the hydrolyzed. It increases the consumption of demineralized water and causes fluctuations in the operation of hydrolyzed, resulting in changes in the amount of ammonia produced by the hydrolyzed, and at the same time, affecting the removal of NO\({}_{\chi}\).
## 4 Conclusions  
The paper presents the results of an assessment of the suitability of waste, wheat and wheat-rye sourdough bread for use in ethanol production. The assessment considers both the efficiency of the process and the quality of the resulting spirit distillate. The following methods were employed for the preparation of the mashes and their fermentation: pressureless starch liberation combined with separate hydrolysis and fermentation (PLS-SHF), pressureless starch liberation combined with simultaneous saccharification and fermentation (PLS-SSF) and native starch hydrolysis without preliminary starch 'activation' (NSH-N/A) or with starch 'activation' (NSH-A) at a temperature not exceeding the starch gelatinisation temperature.  
The intake of sugars was within the range of 96-99%, while fermentation efficiency reached a level between 83 and 96% of the theoretical efficiency. The observed differencesin these indicators were dependent on the method of preparing the mashes, but the type of bread used had no significant effect on the final ethanol yield.  
At the planning stage of the experiments, the method based on native starch hydrolysis appeared to offer a promising alternative to the traditional temperature-assisted treatment combined with two-step enzymatic hydrolysis. However, the results of the experiments demonstrated that the fermentation efficiency following native starch hydrolysis (NSH) was statistically significantly lower than that observed for the pressureless starch liberation (PLS) method. The application of the separate hydrolysis and fermentation (SHF) method resulted in a prolonged technological procedure, yet the process efficiency was not significantly improved.  
Summarising the results and observations described above, it can be concluded that the optimal solution in terms of time, energy consumption and fermentation efficiency for both types of bread is the utilisation of the pressureless starch release method combined with simultaneous saccharification and fermentation (PLS-SSF) for the preparation of mashes.  
Conceptualization, M.B. methodology, M.B.; software, K.P-P.; validation, K.P.-P.; formal analysis, M.B. and U.D.-K.; investigation, A.O. and M.K.; resources, M.B.; data curation, M.B. and K.P.-P.; writing--original draft preparation, M.B.; writing--review and editing, U.D.-K.; visualization, A.M.P.; supervision, U.D.-K.; project administration, M.B. and A.M.P.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 1 Introduction
### Study Objective and Research Gap  
While significant progress has been made in developing deep-learning-based methods for land cover classification [24; 25; 26; 27], a notable gap remains in the availability of remote sensing datasets tailored to agricultural regions [32; 33; 34]. To address this gap, this study presents a cost-effective methodology for preparing semantic segmentation datasets, focusing on reducing the time and costs traditionally required for data collection. This methodology enhances reproducibility and provides an effective approach to generating datasets for land cover mapping in vineyard ecosystems.  
Our study focuses on mapping different land cover classes, including grasslands, herb-dominated habitats, and tree-dominated areas in the Franciacorta region of Lombardy, Italy. By using remote sensing and artificial intelligence, we introduce a methodology for mapping large areas, laying the groundwork for future studies on how surrounding land cover can contribute to the ecological balance and sustainability of vineyard ecosystems.  
While significant progress has been made in developing land cover classification using semantic segmentation, a notable gap persists in creating comprehensive remote sensing datasets tailored to agricultural regions. To bridge this gap, this study presents a cost-effective methodology for preparing a semantic segmentation dataset to ensure reproducibility for other researchers. We introduce a novel land cover dataset comprising seven manually annotated classes, specifically designed for large-scale semantic segmentation, with a particular focus on the wine cultivation area. These essential classes include grasslands, arable land, herb-dominated areas, hedgerows, vineyards, tree-dominated man-made habitats, and olive groves.  
The dataset was created through the analysis of a large agricultural area, specifically the Franciacorta region of Lombardy, Italy. Landsat satellite RGB images with a 30 m resolution, obtained using Google Earth Pro 7.3.6 software, served as the image source for in-depth analysis.  
In this paper, we conducted a rigorous comparative analysis of state-of-the-art deep-learning-based segmentation models to determine the optimal approach. We employed a variety of models, including UNet, SegNet, and DeepLabV3, combined with different backbone convolutional neural networks.
## 4 Performance Analysis
### Overhead Estimation  
In order to quantify the overhead signaling required for the operation of the scheme under consideration, two performance metrics will be adopted, namely, the average NLE and the SP.
# Wearable Inertial Devices in Duchenne Muscular Dystrophy: A Scoping Review
## 4 Discussion
### Clinical Perspective  
All articles demonstrated meaningful relationships between clinical and instrumental evaluations. Experimental tests focused on the estimation of correlation between objective outcomes monitored by the inertial sensors and the clinical scores assigned through functional scales, revealing moderate (\"r\" or \"rho\" > 0.5) and strong correlations (\"r\" or\"rho\" > 0.7). These promising results were obtained for tasks involving both upper and lower limbs.  
An important aspect that should be emphasized is the complementary relationship between clinical and instrumental evaluation. In fact, wearable sensors and related outcome measures can be used to support the functional analysis conducted by the physiotherapist, not to replace it. Functional outcome measures for DMD patients in clinical trials have traditionally consisted of timed tests and motor scales performed during hospital visits (6 MWT, NSAA, Brooke, PUL) and they proved to be reliable and reproducible. A common disadvantage of traditional scales is that they may not capture real-world conditions. Additionally, multiple external factors such as motivation, fatigue, time of day, age, developmental stage, and behavior are known to influence performance in these clinical assessments. Moreover, clinical evaluation could be affected by the physiotherapist's subjective opinion. Innovative, technological, and wearable tools provide the opportunity to identify outcome measures that are objective and clinically relevant. The implementation and validation of qualified, digitally measured endpoints enables accurate and continuous assessment of the progression of the pathology and related motor dysfunctions, the evolution and effect of endurance during different periods of the day, or the potential benefits of an investigational treatment in a real-world setting.