esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 2. _Argania spinosa_ Characteristics
### Ecological Feature  
The Argane stands are a thermophilic and xerophilic species that thrive in specific conditions of temperature and humidity, typically favoring a mild climate. Most Argane proves are located in the Mediterranean-Saharan transition zone [25], and the tree naturally grows in warm and temperate arid to semi-arid zones [2], including Saharan regions in southern Morocco. It exhibits remarkable resilience to the arid conditions of southwest Morocco, capable of enduring temperatures ranging from 30 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, with the average temperature during the coldest month in its habitat ranging from 3 \({}^{\circ}\)C to 7 \({}^{\circ}\)C [2]. The Argane stands have demonstrated its ability to withstand short-duration extreme minimum temperatures, reaching as low as \(-\)2.6 \({}^{\circ}\)C in 1955. Moreover, it can thrive with minimal rainfall and can be found on steep slopes across a wide latitudinal range, spanning from sea level to approximately 1500 m [22]. The yearly rainfall within its primary range varies from 150 to 400 mm [1]. The Argane stand's deep-rooted nature allows it to access water from considerable depths in the soil. The Argane stands exhibit adaptability to various soil types, including on shallow, rocky, schist, quartzlite, limestone, and alluvial soils [26, 27]. However,  
Figure 2: Natural geographic distribution area of Argane forest stands in Morocco.  
it tends to avoid mobile sand, soils, which can lead to root scouring and detrimentally impact its root system [26].
# Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
## 3 Simulation Results
### Simulation Results of a Two-Color Scheme  
The generation of two-color FEL pulses with extraordinary brightness presents new prospects for numerous pump-probe experiments in the VUV to hard X-ray spectral regions. Inspired by [30; 41], a similar two-color method has been proposed and simulated based on CPA-EEHG.  
The amplitude of the bunching factor in EEHG can be found in [40]. At a given harmonic order and the seed lasers, the strengths of two dispersion section dominate the bunching factor, which further influences the FEL radiation proprieties. A relatively modest change in the settings of the dispersion strengths will result in the \"pulse splitting\" effect [42; 43; 44], which has essential impacts on the output-pulse profiles. With the increasing of two dispersion strengths, the pulse will split into two well-separated sub-pulses since the electrons at the pulse center are overwhelmed, while those close to the head and tail are optimally bunched. These two sub-pulses will exhibit distinct carrier wavelengths due to the considerable frequency chirp present in the initial seed pulses.  
In the simulation, the dispersion strengths \(R_{56}\) are increased to \(\sim\)4.03 mm and \(\sim\)0.22 mm, while the seed lasers remain unchanged. The Wigner distributions of the FEL pulses before and after the compressor are demonstrated in Figure 9. The pulse durations of the two compressed pulses were effectively reduced from 14.4 fs and 51.3 fs to 4.5 fs and 7.2 fs, respectively. The peak powers can be maintained at approximately 1 GW, which is sufficient for the majority of two-color pump-probe experiments. The generated FEL twin pulses have a relative time delay of about 109 fs and central wavelengths of 13.63 nm and 13.39 nm. Figure 10 shows the variations of the FEL pulse properties when the gratings are rotated. The relative time delay of the compressed FEL twin pulses can be precisely controlled by tuning the azimuth angle, which might be of great benefit to certain two-color pump-probe experiments.  
Figure 9: The Wigner distributions of the FEL pulses before (**left**) and after (**right**) the compressor.
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 3 Our Proposed Method
### Description of Our Approach  
Since our interest is to predict activities based on time and location, the sensor information, i.e., sensors that are being activated (column 3 in Table 1), are not considered. Each user-interaction event \(a_{i}\) is therefore represented by a triplet \((X,Y,Z)\) in ASCII character, where \(X\) is the time, \(Y\) is the location and \(Z\) is the activity performed.  
Referring to the example of input string '_sensorsensor_', PPM builds the context models based on the number of preceding characters used. Assume that \(a_{1}=(s,e,n)\), \(a_{2}=(s,o,r)\), \(a_{3}=(s,e,n)\) and \(\ldots\), we want the highest context model (\(k=2\)) to predict user activity (\(n\) in \(a_{1}\)) based on time and location  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Order \(k=2\)**}} & \multicolumn{8}{c}{**Order \(k=1\)**} & \multicolumn{8}{c}{**Order \(k=0\)**} & \multicolumn{8}{c}{**Order \(k=-1\)**} \\ \multicolumn{1}{c}{**Predictions**} & **c** & **p** & **Predictions** & **c** & **p** & **Predictions** & **c** & **p** & **Predictions** & **c** & **p** & **Predictions** & **c** & **p** \\ \hline en & \(\rightarrow\) & s & 2 & \(\frac{2}{3}\) & e & \(\rightarrow\) & n & 2 & \(\frac{2}{3}\) & \(\rightarrow\) & e & 2 & \(\frac{2}{17}\) & \(\rightarrow\) & A & 1 & \(\frac{1}{|A|}\) \\  & \(\rightarrow\) & _esp_ &  
(\(s\) and \(e\) in \(a_{1}\)). To do this, PPM is trained on each \(a_{i}\) rather than on the entire sequence of input string. With this, the \(k=2\) model will have two predictions: (1) \(se\to n\) and (2) \(so\to r\).  
One of the issues with temporal resolution is that the scale on which it is measured can in fact change the analysis. If a representation would to train on data where the user was in the bathroom showering at 9.15 a.m. and then head to the kitchen to make coffee at 9.50 a.m., the training data for these two events would be:  
(_9.15 a.m., bathroom, showering_), (_9.50 a.m., kitchen, making coffee_),...  
If the test data indicated that the user was showering in the bathroom at 9.05 a.m., the model would not be able to make a prediction of the user location at 9.05 a.m. since the model does not have any information of the user showering in the bathroom precisely at 9.05 a.m. In view of this, the time is processed as 30 min intervals, i.e., mapping each ASCII character for each time interval of 30 min. When the PPM model is queried for a prediction of where the user is likely to be at 9.05 a.m., which falls within '9 a.m.-9.29 a.m.' interval, it can predict that the user is in the bathroom. Figure 2 shows the representation of Table 1 in ASCII characters and processed based on 30 min time intervals.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 4 Experimental Simulation Results and Analysis  
### Data Analysis  
In the process of exploratory data analysis, feature engineering is a very important step. In the process of feature engineering, the characteristics that can better represent the core of the issue and play a greater role in the prediction process will be chosen from the original features. These selected features are put into the model to improve the prediction accuracy and speed of the model. It is necessary to select features that highly correlate with 'blockage' in the dataset to conduct experiments.  
The correlation matrix between eigenvalues is shown in Figure 9. It can test the direction and degree of a change trend between any two variables. Covariance is a parameter that measures the magnitude of the mutual influence of two variables. As shown on the right side of Figure 9, the lighter the color in the picture, the larger the correlation coefficient value between the two features, and the darker the color, the smaller the correlation coefficient. The larger the absolute value, the higher the correlation between the two. For this project, there are 21 features and one label. Covariance matrices work well for multidimensional problems like this, and correlations between two features can be seen by looking at the intersection between any two features. Therefore, the degree of correlation between each feature and 'blockage' can be analyzed by looking at the intersection value of 'blockage' and other features.  
Figure 9: Correlation coefficient between features.  
The range of correlation coefficient is \(-1\) to \(+1\). It can be calculated by the formula [20]  
\[\mathrm{r_{xy}}=\frac{\mathrm{n}\sum\mathrm{x_{i}y_{i}}-\sum\mathrm{x_{i}}\sum \mathrm{y_{i}}}{\sqrt{\mathrm{n}\sum\mathrm{x_{i}^{2}}-\left(\sum\mathrm{x_{i} }\right)^{2}}\sqrt{\mathrm{n}\sum\mathrm{y_{i}^{2}}-\left(\sum\mathrm{y_{i}} \right)^{2}}} \tag{14}\]
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 4 Results
### Additional Measurements Sites: Assessment of the Group Components  
Measurements of both traffic noise and vehicle flow have been performed in randomly chosen locations within Z9 in order to validate the Dynamap predictions. The analysis  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Site** & **Address** & **Site** & **Address** & **Site** & **Address** \\ \hline
1 (5) & Via Suzzani & 8 (3) & Via Chiese & 15 (4) & Via Gregorovius \\
2 (2) & Via Bernina & 9 (5) & Via Moro & 16 (4) & Via Catone \\
3 (3) & Via Ciaia & 10 (1) & Via Marchionni & 17 (6) & V.le Sarca \\
4 (3) & Via Cosenz & 11 (1) & Via Gabbro & 18 (1) & Via Boschi Di Stefano \\
5 (5) & Via Majorana & 12 (2) & Via Pastro & 19 (6) & Via Murat \\
6 (3) & Via Maffucci & 13 (4) & Via Bauer & 20 (1) & Via Sarzana \\
7 (2) & Via Ippocrate & 14 (2) & Via Polvani & 21 (3) & Via Cosenz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Location of the measurement sites (cf. Figure 6). The group index \(g_{i}\) is indicated in parentheses [91].  
Figure 7: Mean normalized hourly cluster profiles, \(\overline{g_{k}}(h)\) [dB], vs. day hour \(h\), from the 24 Dynamap sensors (see Equation (12)). Here, \(k=1,2\) indicates the cluster index. The corresponding \(\pm\) standard deviations are indicated by the colored bands [91]. Notice the shift of the time axis with respect to Figure 3.  
is also aimed at determining absolute values of the errors involved in the predictions, and from the detail comparison with the measurements being able to evaluate the limitations of the method. The locations and the corresponding groups are reported in Table 4.  
In Table 5, we report calculations of the contributions from different groups, \(L^{(1h)}_{\text{eq}}(t_{\text{ref}}s(g_{i}))\), to the local equivalent level, \(L^{(1h)}_{\text{eq}}(t_{\text{ref}}s)\), at selected locations \(s\) corresponding to some of the measurements sites listed in Table 4. These results have been obtained by setting \(\delta^{(\tau)}(t,g_{i})=0\) in Equation (10), since we are dealing with the static maps (BTNMs) at the reference time \(t=t_{\text{ref}}\).  
As one can see from Table 5, the major contribution to the local site level comes from the group \(g_{i}\) which the site belongs to. This can be clearly seen by comparing the values of \(L^{(1h)}_{\text{eq}}(t_{\text{ref}}s(g_{i}))\) for different groups. For example, for Site 1, which belongs to group \(g_{5}\), the most significant contribution comes from \(L^{(1h)}_{\text{eq}}(t_{\text{ref}},s(g_{5}))\). However, each local site level is subject to the influence of nearby streets through other groups, as it is apparent from Table 5. In particular, for Site 10, the maximum contribution is due to \(L^{(1h)}_{\text{eq}}(t_{\text{ref}},s(g_{2}))\), although the site belongs to group \(g_{1}\). This is the result of the complex noise environment around Site 10, and it may be expected to have further consequences regarding the model predictions.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 5 Results
### ICI Unaware Iterative Receiver
#### 5.1.2 Experimental Results  
The performance of the iterative reception algorithm in the underwater acoustic MIMO-OFDM communication system is verified by a pool experiment. The experiment was conducted in the pool of Harbin Engineering University, China. The pool is about 45m long, 6m wide, and 5m deep. The transmitting array has 2 transducers. The first transducer is placed around 2m below the water surface and the second one is placed around 3.3m below the water surface. The distance between the two transducers is kept around 1.3m. Due to the high transmission response of the transmitters and the high sensitivity of the receiving hydrophones in the experiment, the signal generator is used to directly send the signal to the transmitters without the use of an amplifier and also the received signal is collected directly. There are two vertical arrays, A and B, at the receiving side, each having 14 hydrophones. The acquisition channel sequence is such that the receiving vertical array A receives 114 channels, while vertical array B receives 15-28 channels.  
Each data frame contains 8 OFDM symbols, QPSK mapping is adopted and the coding method is 1/2 code rate LDPC code. Combining the received data of different hydrophones of the vertical arrays A and B, the greater the number of combined channels, the greater the signal to noise ratio, thereby improving the processing performance. Select the 2 nd, 4 th, 6 th, 11 th, 16 th, 18 th, 19 th, 20 th, and 25 th receive hydrophones orderly to receive signals in 10 frames per sequence. We compared the performance of the basic turbo iterative receiver with the channel estimation outside the loop and the proposed iterative receiver with channel estimation inside the loop by combining a different number  
Figure 4: Bit error rate (BER) simulation performance comparison of the proposed iterative receiver.  
of hydrophones, as shown in Figure 5. It can be seen that in the basic turbo receiver, the iterative processing does not improve the performance of the system and the BER increases when 6 and 7 hydrophones are combined. The BER is even worse than the case where the non-iterative receiver was used. The reason behind this is that the initial channel estimation may have large errors in this experiment, resulting in poor performance for the iterative receivers that do not include channel estimation. Part (b) of the Figure 5 shows the BER performance of the proposed iterative receiver with the increase in the number of hydrophones for each iteration, and it can be seen that as the number of iterations increases, the system performance is significantly improved, and the performance tends to get stable after three iterations. Furthermore, if we increase the number of merged hydrophones for data collection, the effect of the iterative reception algorithm gets more obvious.
# Wideband Versatile Receiver for CubeSat Microwave Front-Ends
## 1 Introduction
The wide bandwidth, i.e., 2-18 GHz, and the employed components make the system very interesting for a great number of applications, e.g., from satellite communication to radar front-ends [28; 29; 30; 31; 32]. The paper is organized as follows. The main works related to this topic have been depicted in Section 2. The operating principle, simulations, and characterization of the components of the chain have been reported in Section 3. In Section 4, the performance of the receiver was shown. Finally, the conclusions are drawn in Section 5.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 6 Understanding Inter-Sensor Differences
### SLSTR Inter-Comparisons  
Inter-comparisons of SLSTR-A and B measurements allow verification of the data from thermal infrared channels that would only have been possible with the tandem mission to a level of fidelity not usually possible with standard comparisons using simultaneous-nadir overpass data. At thermal infra-red (TIR) wavelengths, such comparisons are ordinarily difficult because of atmospheric effects and the temporal variability of the surface temperatures, and particularly so for the 55\({}^{\circ}\) off-nadir oblique view, where angle matching with other satellite sensors is not generally possible.  
The analysis of the tandem phase datasets allows investigation into potential instrumental effects. These include:  
* Sensor-to-sensor differences. Results are generally found to be within the expected uncertainties, except for channels S8 and S9 in the oblique view, which show differences vs. temperature (Figure 6). These results are consistent with the presence of an internal stray light effect observed during the SLSTR pre-launch calibration campaign [25]. The results of this tandem-phase analysis show the potential benefits of a stray-light correction.
* Sensitivity to total column water vapor can indicate an issue with sensor spectral response function characterization. No apparent sensitivity is found in the tandem phase data.  
The tandem data also provides the opportunity to investigate the time series of differences and connect them to onboard events (see [25] for more details). Figure 7 presents a small transient drop of the S3A/S3B difference observed in early August 2018. This event has followed a change in the S3A  
Figure 5: Inter-calibration model for the relative error of OLCI-B with respect to OLCI-A, obtained after fitting a 5 th order polynomial for the measured differences for each camera. The estimated uncertainty on the model is better than 0.5%, an achievement that would not be possible without tandem operations. Reprinted from [21].  
focal plane temperature set point and an S3B outgassing operation, both of which may explain the observed change. Such an effect has never been directly observed before, and indeed has been made visible only, thanks to the tandem configuration.  
Figure 6: Differences in oblique view S8 (**left column**) and S9 (**right column**) brightness temperatures (reprinted from [25]). The top-frames show the as-measured results compared against the stray-light model. The lower frames show the results after correction.  
Figure 7: Time series of differences between SLSTR-A and SLSTR-B for the S9 infrared channel in the nadir view for night-time data for 280 \(\pm\) 0.5 K scenes during the tandem phase. The time series shows the coincidence of the evolution of the detector temperatures of the instruments and the measurement differences. For SLSTR-A, the set point of the detectors is adjusted by 1 K, while for SLSTR-B, decontamination is performed, both for operational reasons.
# A Graph-Cut-Based Approach to Community Detection in Networks
## 3 Methods
### Network and Community Structure  
To describe the problem and the proposed algorithm precisely, some basic notations are introduced.  
**Definition 1**.: _A network is a graph \(G=(V,E)\) defined by \(V\) and \(E\), where \(V\) and \(E\) are the set of vertices (nodes) and the set of edges, respectively. An edge is a pair of two vertices. Given a network \(G=(V,E)\) and a subset \(C\subset V\) of vertices, \(E(C)\subset E\) is a subset of \(E\) consisting of all the edges whose endpoints belong to \(C\)._  
This paper concerns undirected graphs without loops or multiple edges. even though the proposed method may be easily extended to the case of directed graphs. Therefore, an edge \(e=\{u,v\}\in E\) is a pair of two vertices \(u,v\in V\), and the order of the two is not relevant unless it is noted otherwise. We also assume that the graph is _connected_; every pair of vertices \((u,v)\) of \(G\) has a path from \(u\) to \(v\). In this paper, networks are used interchangeably with graphs.  
**Definition 2**.: _A community structure \(C=\{C_{1},C_{2},\ldots,C_{k}\}\) of a network \(G=(V,E)\) is a partition of \(V\), the set of vertices of \(G\). In other words, \(\cup_{i=1}^{k}C_{i}=V\) and \(C_{i}\cap C_{j}=\emptyset\) for \(i,j=1,\ldots,k\) with \(i\
eq j\). Here, \(k\) refers to the number of communities of the community structure \(C\)._  
With these definitions, the objective of the community detection problem can be stated as finding a good community structure \(C\) when a network \(G\) is given.
### Contributions of the Proposed Work  
Different intrusion datasets are used to train and assess the several DRL-based IDS approaches that have been reported in the recent research literature [18; 19]. Nonetheless, the majority of the work aims to improve the detection performance compared to other methods of their kind. Many of them only scratch the surface of developing and implementing a DRL-based IDS approach without providing essential details, such as how the DQL agent can be formulated in light of an RL principle or how to fine-tune hyper-parameters for improved self-learning and interaction with the underlying network. Furthermore, in the existing literature, flow and packet-level intrusion detection is usually treated separately by using different methods. In this paper, we intend to exploit the application of RL to provide a unified framework for intrusion detection problems at both the packet level and flow level. We achieve this by strategically formulating intrusion detection problems into an RL problem and then designing the overall IDS structure in a DQN framework. The proposed method can not only detect malicious attacks but also classify their types. To summarize, the contributions of this paper are listed as follows:  
1. By means of a proposed image embedding scheme, network traffic data are transformed into time-series first and then image data so that it can be processed by CNN and 1D-CNN-based architecture of the RL agent to tackle the intrusion detection problem.  
Flow information is attached with the dataset used for the packet-level IDS to incorporate the temporal information for further performance improvement.
* The exploration module is designed based on a CGAN and the \(\varepsilon\)-greedy policy for the flow level IDS.
* An anomaly detection model is designed for the packet-level and flow-level IDS to detect unknown novel attacks.
* In order to improve the robustness of the final detection, a sample agent with a complement reward policy of the RL agent is introduced in the RL framework for adversarial training.
* Finally, since RL is known to be sensitive to the hyper-parameters chosen, in this paper, a Bayesian search is conducted to find the optimal parameters.  
In the remainder of the paper, Section 2 includes a brief review of related work. Sections 3 and 4 introduce in detail the proposed intrusion detection frameworks at the packet level and flow level, respectively. Section 5 includes results for both packet-level and flow-level experiments. Finally, Section 6 draws conclusions and discusses future work.