esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Frost events during the growing season can significantly impact vegetation function and structure. Solar-induced chlorophyll fluorescence (SIF) and the normalized difference vegetation index (NDVI) are two widely used proxies for measuring vegetation growth. However, the extent to which NDVI and SIF respond to frost events and how the responses vary under different temperature, precipitation, and shortwave radiation conditions are still unclear. In this study, spatially gridded meteorological data were employed to identify frost events during the growing season in the Third Pole. Subsequently, vegetation responses to the frost events were examined using remotely sensed SIF and NDVI data in different seasons in the Third Pole. During the growing season, the number of frost events declined faster from 2001 to 2009 than from 2010 to 2018. From 2001 to 2009, most alpine vegetation areas in the Third Pole exhibited greening trends. SIF exhibited a strong correlation with environmental factors and showed higher sensitivity to environmental factors compared to the NDVI. Over the past two decades, the impact of temperature and frost days on alpine vegetation has decreased while the impact of precipitation and radiation has increased. This suggests that the control mechanisms governing alpine vegetation are gradually shifting in response to ongoing climate change in the Third Pole. This study enhances our comprehension of frost changes in alpine regions during the growing season and enriches our understanding of how alpine vegetation responds to climate change.  
frost days; Alpine vegetation; GOSIF; NDVI; Third Pole +
Footnote †: journal: Remote Sensing
# Coordinate Descent for Variance-Component Models
## 4 Numerical Results
### Simulations
#### 4.1.1 Low-Rank  
We now present the results for where \(\mathbf{V}_{i}\) are generated as stated above and \(r<n\). As \(\mathbf{V}_{i}\) are low-rank, we run the Woodbury implementation of PXI-CD and exclude PX-CD as it has the same computational cost as PXI-CD for each update and exhibits almost identical performance. First, PXI-CD is run until the relative change \(\left[L(\mathbf{\gamma}^{(t+1)})-L(\mathbf{\gamma}^{(t)})\right]/\left[\ |\ L(\mathbf{\gamma}^{(t)})\ |\ +1\right]\) is less than \(10^{-10}\) and we store the final objective value as \(L^{*}\). We then run the algorithms MM, EM and FS and terminate once \(L(\mathbf{\gamma}^{(t)})>L^{*}\). We initialize all algorithms to start from the point \(\mathbf{\gamma}^{(0)}=\mathbf{1}\). Each simulation scenario is replicated 10 times. The mean running time is reported along with the standard error of the mean running time provided in parentheses.  
The results of the low-rank simulations are given in Tables 1-3. The results indicate that, apart from the smallest scale problem when \(r=m=20\), our PXI-CD algorithm outperforms the MM algorithm and significantly so that the scale of the model (both \(m\) and \(r\)) increases.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & & \(m\) & \\ \hline
**Method** & \(\mathbf{r}\) & **20** & **50** & **75** & **100** \\ \hline PXI-CD & 20 & 4.20 (0.07) & 17.00 (1.18) & 25.81 (1.57) & 51.8 (5.09) \\ MM & & 1.89 (0.22) & 42.54 (7.69) & 220.06 (61.19) & 845.2 (136.89) \\ EM & & 23.30 (0.52) & - & - & - \\ FS & & 91.80 (0.99) & - & - & - \\ PXI-CD & 50 & 5.42 (0.16) & 21.36 (0.85) & 37.46 (2.72) & 40.21 (2.97) \\ MM & & 95.63 (41.20) & 203.24 (53.20) & 584.76 (53.20) & 1167.22 (114.38) \\ PXI-CD & 100 & 8.96 (0.25) & 30.17 (2.24) & 28.26 (0.73) & 45.65 (2.56) \\ MM & & 112.58 (21.48) & 555.38 (82.01) & 699.89 (90.55) & 1327.54 (53.80) \\ PXI-CD & 150 & 14.13 (0.63) & 33.18 (4.67) & 38.24 (1.54) & 48.97 (2.08) \\ MM & & 122.44 (38.48) & 628.24 (55.61) & 929.67 (75.87) & 1243.97 (84.30) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The running times (s) when \(\mathbf{V}_{i}\) are low-rank, \(n=1000\), and \(\gamma_{0}=0.1\).
## 6 Simulation Results and Discussion
### Throughput Comparisons
#### 6.1.1 Throughput Comparisons for the Random Topology  
For the first scenario, we measured the stream parts processed by the system's nodes. Each stream part was considered as a small group of 50 tuples. We assumed that the first (most expensive) transferring steps defined by the scheduling matrix took more time than their processing, while the last steps (less expensive steps) took less time than their processing. For the most expensive steps defined by the \(SM\), no buffer space was required: the data streams that arrived to the nodes found them idle, so the processing stage was activated immediately and processing started. For the less expensive steps, the data streams had to be buffered, waiting for the completion of processing of the data streams which previously arrived. When more buffering was required, the throughput performance decreased as increasing numbers of data streams were kept waiting in the buffers. Generally, our strategy used less buffer memory as a result of the fact that each node received streams from exactly one other node at each step; thus, some care was taken regarding the data streams received by each task. Moreover, our pipelined scheme reduced the overall time (transferring, processing, packing), as explained in Section 5. This increased the throughput of our work. A disadvantage of [PERSON]'s strategy is the task migration which occurred and added extra overheads to its overall performance. The default Storm strategy has no particular mechanisms to organize the data being transferred; therefore, its performance is generally the worst among all the competitors.  
The effect of buffering is clearer in the second scenario. The three strategies appeared to behave in a similar manner, but the number of tuples being processed decreased by an average of about 25%. When \(v>u\), the data streams were transferred quickly, while processing took longer. Again, our strategy outperformed the others due to the data transferring organization in steps and due to pipelining, which reduced the overall processing times.  
Finally, we have to emphasize the effects caused by the nature of each application's topology. When the topology is random, such as the one in Figure 1, then there are cases in which one task may receive data streams from several tasks at the same time. This is also true for our scheme: during a communication step, two tasks that belong to the same source node may transfer the data streams to the same task that belongs to the target source. In the example shown in Figure 1, this can happen during the communication between bolts 2 and 3 and bolt 4. If tasks E, J belong to the same source node and task M belongs to the target node, then two data streams would be transmitted to M during the internode communication. Necessarily, one of the streams has to be buffered. As will be seen in the next set of simulations, all the schemes performed better for the linear topology.  
Figures 4 and 5 show the throughput performance for the random topology under the first and second scenarios, respectively.
## 4 Computer Simulation Results
### Experimental Dataset
#### 4.1.1 Speech Source Parameters
The different microphone sensors 'coordinates are taken to be (7.11, 6.0), (7.11, 5.55), (7.11, 5.10), (7.11, 4.65), (7.11, 4.20) and (7.11, 3.75). The motion of the speakers is modelled by the Langevin model. Details about the Langevin model can be found in [6] and the references therein. The state equations governed by the Langevin model can be expressed as follows:  
\[\alpha_{k+1}^{pos}=\alpha_{k}^{pos}+\varnothing.\alpha_{k}^{vel} \tag{41}\]  
\[\alpha_{k+1}^{vel}=e^{-\beta\varnothing}.\alpha_{k}^{vel}+\vartheta.\sqrt{1-e^{ -2\beta\varnothing}}.\Sigma_{k} \tag{42}\]  
In (41) and (42), \(\alpha_{k}^{pos}\) and \(\alpha_{k}^{vel}\) represent the 3D position vector and velocity vector of the target speaker in motion. The terms \(\beta\), \(\phi\) and \(\Sigma_{k}\) represent the rate constant which controls the rate of decay of velocity, discretization time step interval, and process noise, respectively. The process noise \(\Sigma_{k}\) is a 3D Gaussian vector with mean zero and covariance, \(\sigma_{\Sigma}.\sigma_{\Sigma}^{T}\), where \(\sigma_{\Sigma}\) represents the standard deviation of the process noise. The Langevin model parameters used for the simulation are included in Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**Room Dimensions in x,y Coordinates (in m)** & **X (14 m)** & **Y (22 m)** \\ \hline SNR (in dB) & 0–60 (training) & 15, 35, 80 (testing) \\ \hline Reverberation time, T60 (in ms) & 50, 250, 550 & 550 \\ \hline Microphone array & Linear & 6 sensors per array \\ \hline Number of speakers & 3 (training) & 3 (testing) \\ \hline \end{tabular}
\end{table} 
Table 1: Acoustic scene parameters.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 4 Proposed HDLNIDS Model
### Data Preprocessing  
In the data pre-processing phase, initially, the network traffic was categorized and preprocessed. All necessary conversions for the HDLNIDS and IDS-compatible file formats were performed during pre-processing. The different characteristics, such as timestamps and network Internet Protocol (IP) addresses, have little effect on whether network traffic is malicious in the original CICIDS-2018. As timestamp features are utilized to keep track of when malicious communication occurs and give only minor assistance while training the algorithm, we eliminated them during the pre-processing step. Similar to an anomalous intrusion detection system (AIDS), the majority of traffic should be classed based on its behavior, without bias or competing with the IP address; thus, we deleted the IP address features as well. Pandas, NumPy, and Scikit-learn packages within the Python language were used to implement data pre-processing tasks.  
The subject of class inequality has received significant attention from the research community. A class imbalance is caused by inadequate data distribution; one class has the majority of the samples, while others have comparably few. Because of unlimited data values and imbalanced classes, the classification issue becomes more challenging as data dimensionality grows. [PERSON] et al. (2018) used a variety of ML methods to address the class imbalance problem. [PERSON] et al. (2018) investigated numerous methods for the  
Figure 1: Proposed HDLNIDS model.  
problem of class imbalance. Most algorithms target the majority of data samples while missing the minority of data samples. Hence, minority samples emerge in an irregular but consistent manner. The basic strategies to resolve the data problem include data preprocessing and feature selection, and each approach has advantages and disadvantages. The ID dataset has a problem with high-dimensional imbalance, which includes lacking interesting features, attribute values, and the only availability of cumulative data. The data appear to be unreliable, with inaccuracies and anomalies, as well as unpredictable, with variations in codes or names. To overcome the imbalance problem, we utilized over-sampling, which included increasing the frequency of occurrences among the minority group by indiscriminately reproducing them to enhance the representation of the minority group in the dataset.  
Although there is a risk of overfitting with this process, no records were lost, and the over-sampling approach outperformed the under-sampling option. Table 1 presents the extracted features during preprocessing phase.
# A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate
## 4 IF Module Configuration  
To achieve a high data rate and exploit the potential of the THz wide frequency vacancy, IF frequency modulation is needed to convert multiple base-band channels to a higher frequency with sufficient spectral capacity. Figure 4a shows the schematic block diagram of the IF up-converter module while the down-converter has a symmetric structure. Any two of four IF channels connect with one FPGA platform and each channel occupies the 2.5 GHz bandwidth. Four IF channels, marked as CH1 to CH4 in Figure 4, are up-converted to the IF frequency range of 15.65 \(\pm\) 6.25 GHz via a two-stage frequency conversion configuration. In the first stage, CH1 and CH2 are modulated by a 2.5 GHz LO signal to the 1.25-3.75 GHz range while CH3 and CH4 are converted to the 3.75-6.25 GHz frequency. The up-converted CH1 is then combined with CH3 and up-converted to the lower side band (LSB) frequency of the second stage LO signal at 15.65 GHz, from 9.4 to 14.4 GHz, and CH2 in combination with CH4 are modulated to the upper side band (USB). The LSB and USB channels are then combined with the power coupler and connected to the interface of the THz front-end. Lowpass and bandpass filters are applied to individual channels for interference suppression. The spectrum assignment of the four channels in the IF operation frequency is shown in Figure 4b. The total IF operation frequency range is therefore 9.4 to 21.9 GHz, with 10 GHz of effective spectral occupancy. Compared with the systems without the IF conversion stage [18; 19; 20], the proposed approach features a larger bandwidth, which results in a higher data volume.  
A loopback test was performed with direct connection of the base-band and IF modules, and the measured results are shown in Figure 5. The performance of the communication is evaluated based on the measured EVM and BER. In the case of averaging a large  
Figure 4: (**a**) Schematic of the IF module, and (**b**) the frequency assignment of the base-band channels.  
number of symbols, it is safe to assume that EVM (dB) \(=-\)SNR (dB) [27]. The measured constellation is shown in Figure 5a with an EVM value at \(-\)19.7 dB (10.3%), equivalent to SNR at +19.7 dB. The BER test with real-time Ethernet traffic from the Spirent TestCenter is also measured with the variation of SNR, as shown in Figure 5b, which shows a BER around \(10^{-8}\) when SNR is at 19.7 dB.
## 4 Conclusions  
The proposed study presents a comprehensive approach to perform segmentation and identification of the vertebrae using CT scans, which utilizes a combination of deep learning and traditional machine learning techniques. The proposed framework shows high accuracy and robustness in segmenting lumbar and thoracic vertebrae by achieving a model accuracy of 93.62% on the VerSe'20 dataset. The incorporation of [PERSON]'s method for feature extraction further enhances the segmentation process by effectively separating the vertebrae from the background. The results demonstrate that the proposed method outperforms many existing segmentation techniques by achieving a DSC value of 87.10% \(\pm\) 3.72%. This indicates that the Res U-Net model is not only accurate but also reliable across diverse CT scan datasets. Future research can focus on optimizing the model's architecture and exploring additional data augmentation techniques to further enhance segmentation accuracy and robustness.  
The methodology and findings of the proposed study contribute to the research in medical image analysis, which highlights the importance of integrating multiple techniques to achieve superior results. Accurately extracting the features of the vertebrae is crucial for assisting medical professionals in conducting precise preoperative planning. Detailed information about the vertebrae's structure allows surgeons to visualize the anatomy effectively which is essential for making informed decisions before surgery. Future studies using these segmented images as a reference will enable us to identify and highlight the key anatomical features of the vertebrae, including dimensions and orientations critical during spinal fusion procedures. By marking these key points this method has the potential to improve surgical outcomes and reduce complications by providing a clear roadmap for surgeons during operations. Future research should focus on advanced feature extraction techniques and integrating multi-modal imaging data. Developing a real-time implementation for clinical settings and testing on diverse datasets will ensure model generalization. Also in the future, there is a need for research that focuses on creating a user-friendly interface for clinicians and to conduct longitudinal studies to understand the long-term clinical benefits.  
Conceptualization, S., S.G.N. and S.N.B.; methodology, S., M.K., T.P. and S.G.N.; software, S. and V.K.K.; validation, S., M.K., T.P., S.G.N. and S.N.B.; formal analysis, S., T.P. and S.G.N.; investigation, S., V.K.K., T.P. and S.N.B.; resources, S., V.K.K. and S.G.N.;
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 3 Measurement Campaign
### Measurement Setup  
Measurements were carried out in the vicinity of the Poznan University of Technology Lecture Center. In Figure 6, the satellite view of the measurement scenario is shown. Before the building on the square, the trees are planted in a regular grid, with a precise distance of 6 m between consecutive trees in the row. Although six rows of trees can be found in the courtyard, two rows were selected for measurements and marked as Scenarios 1 and 2. The trees in all rows are homogeneous in terms of species. Furthermore, the distance between rows and consecutive trees is regular. The distance between rows is 3 m, while the distance between trees in a row is 6 m.  
The crucial novelty of the measurements was to check tree attenuation when placing both antennas in the same horizontal plane for the alley of trees. Moreover, such a regular order of planted trees allowed for verifying attenuation introduced by at least one and up to six trees in the row. Then, the considered measurement setup was to place the Tx  
Figure 6: Satellite view of the area with considered measurement scenarios. Rectangular areas with trees for two scenarios are marked in two colors. Furthermore, next to Scenario 1, there is a line of length 30 m as the scale.  
antenna at the end of the tree row at a height of 2.2 m, then the Rx at the same height between the tree coronas. The directional horn antennas SL-WDPHN-1840-1719-K were used. The half-power beam width of the antennas was 16 degrees at the carrier frequency of 28 GHz, while the antenna gain for that frequency was 18 dBi.  
The measurement equipment connection was as follows (Figure 7): Continuous wave (CW) was generated by an Anritsu MG3690C at frequency \(f_{c}=\{26,28,30,32\}\) GHz. The transmitted power was 0 dBm. The signal was received by an Anritsu MS2720T spectrum analyzer. The noise floor during the measurement was \(-130\) dBm, and the dynamic range was at least 106 dB in 1 Hz resolution bandwidth (RBW).  
Measurements at all points were automated with Matlab script and remote mode for the measurement devices. Such a configuration allowed us to (i) take measurements for many frequencies and (ii) take multiple measured values per each point, then use the median value as the main one.  
The illustration of the measurement scenario is shown in Figure 8. The Tx position for all measurements was constant, 4 m away from the first tree in the row. Then, the Rx position during the measurements was placed in different free-space points between trees.  
Figure 8: Measurement scenario with tree alley. The Tx (red triangle) is located 4 m from first tree at the front, while the distance between trees in the alley is 6 m. The Rx (yellow circle) is placed between tree coronas; the distance between the trees in the alley is 6 m.  
Figure 7: Measurement equipment configuration overview.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication  
### Introduction  
For the receiver, the challenge lies in optimizing the aperture, field of view, and bandwidth. A large aperture is required to capture as much light as possible given the limited transmission powers, a wide field-of-view is necessary for receiving optical beams from various directions, and a high bandwidth is vital for high-speed data transmission. However, one cannot improve one parameter without tampering with the other. There are two compromises here. The bandwidth of top-illuminated photodiodes, which are commonly used in OWC is inversely proportional to their aperture. This is because with an increase in the area of a photodiode, its capacitance increases, which negatively impacts the bandwidth. The second compromise is between the aperture and the field-of-view, which is caused by the law of etendue. The larger the aperture, the narrower the field-of-view becomes.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 2 Material and Methods
Data from the converters (4 times 2 kS/s) are received by the dsPIC33 microcontroller, where they are subjected to the pipeline signal filtration. Power mains frequency 50 Hz has to be removed as do other electroactive interference, e.g., generated by heart beating excitations. The final step in data processing is calculating the RMS value of the signals as the main diagnostic parameter. The processed data is sent via the Bluetooth module to the master computer, as shown in Figure 7.  
The proposed processing algorithm of EMG signal consists of 3 stages, as shown in Figure 8. The first one is used for removing the low-frequency spectral band, including the ECG electro-cardiac disturbance. The comb filter reduces the impact of environmental interferences mainly generated by the 50 Hz power lines. At first, the low-frequency drift signal is removed by the Band-Pass (BP) Infinite Impulse Response (IIR) filter. In order to achieve a trade-off between effectiveness and complexity of filtering, a 3 rd order filter was chosen. Next, the comb 40 th order filter was implemented to reduce the impact of 50 Hz energy-line interferences. The final stage of the proposed algorithm concerns the RMS signal generation by a parameterized procedure with user-defined offset and window length.