esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 2 System Architecture
### Working Process of the Processor  
The interaction between the CNN processor and the external memory as well as the main processor occurs through two AXI (Advanced eXtensible Interface) interfaces, which are AXI-Stream and AXI-Lite, respectively. The working process of the processor can be divided into three parts: data preparation, computation and results output. For the convolution operation, DMA sends image data, filter data and bias data to the processor through AXI-Stream, and the data are stored in the corresponding buffer. Then, the external main processor writes instruction registers through AXI-Lite, including mode selection (convolution/fully connected computation), image size, filter size, number of channels and startup words. The convolution control finite state machine (FSM) in the controller is selected and generates control signals for PEs, and at the same time, the address generator produces addresses according to the operation parameters to read image, weight and bias data to the PE array for computation. Finally, the output feature maps are stored in the bias buffer and written back to the external memory after the activation operation.  
The fully connected computation is different from the convolution operation. First, the weight data is not stored in the filter buffer because each weight data only participates in one multiplication. Second, the weight data is directly transferred to the PE array through AXI-Stream for computation, which not only reduces the computing time, but also avoids the large storage required for the weight data of the fully connected layer. Furthermore, the PUs is reconfigured to switch to the fully connected computation function. The rest of the process is similar to convolution.
## 3 Experimental Part
### Sample Preparation  
The isolation and concentration of analytes were performed using solid phase extraction (SPE) with a 24-position Visiprep(tm) SPE Vacuum Manifold glass set from SUPELCO and a C-18 (octadecyl-bonded silica) 500 mg sorbent cartridge from J.T. Baker (The Netherlands). The extraction employed methanol with a purity >99.9% (Honeywell, Germany) and LC-MS-grade demineralized water (Honeywell, Germany). Prior to the extraction process, the SPE cartridges were conditioned with 5 mL of methanol and 3 mL of a mixture of demineralized water and methanol, having a solvent composition similar to the analyzed samples. Raw sample volumes ranging from 200 mL to 1 L were enriched with 5% methanol (\(v/v\)) to improve PAH recovery and a surrogate internal standard solution containing deuterated PAHs at a concentration of 100 ng/mL, added in an amount equal to 0.1% of the sample volume, to determine analyte recovery, which might vary between the samples. In this study, the following surrogate deuterated PAHs were used to evaluate PAH recoveries in each snow sample: naphthalene--D8, acenaphthene--D10, phenanthrene--D10, chrysene--D12, and perylene--D12. To each of 5 deuterated PAHs, a certain group of PAH compounds was assigned. Therefore, naphthalene--D8 was used for naphthalene, acenaphthene--D10 for acenaphtylene, acenaphthene, and fluorene,  
Figure 6: Sample analysis steps.  
phenanthrene--D10 for phenanthrene and anthracene, chrysene--D12 for fluoranthene, pyrene, benzo(a)anthracene, and chrysene, and perylene--D12 for benzo(b)fluoroanthene, benzo(a)pyrene, indeno(1,2,3-cd)pyrene, dibenzo(a,h)anthracene, and benzo(g,h,i)perylene. The final results were corrected using the surrogate recovery rate. The analytes were concentrated and purified via solid-phase extraction (SPE) at a flow rate of 5-6 mL/min. The cartridges were dried for 3 min under reduced pressure. Analytes were eluted from the sorbent bed with 2 mL of DCM (in four portions of 0.5 mL each) into pre-weighed chromatographic vials. The extracted samples were analyzed immediately by GC-MS/MS.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Adaptive Extended Kalman Filter (AEKF)  
The adaptive extended Kalman filter is a viable technique for dealing with cases where there is little previous statistical information or where the estimation environment is evolving. Adaptive technology is used in a variety of areas, including adaptive control, in addition to filtering. Deep space probes can boost their navigation efficiency using two separate methods. The first is to create better error models that can represent the navigation system more realistically, and the second is to improve trajectory and sensor error estimation. Only the latter is taken into account in the current issue. To ensure effective navigation efficiency, an adaptive filter for measurement noise estimation is required. In an Inertial Navigation System/GPS (INS/GPS) integrated navigation system, adaptive Kalman filters are used to deal with the issue of unreliable measurement noise variance [110, 111]. In [112], localization and mobile target tracking using TDOA/FDOA measurement with AEKF have been proposed. The framework of localization and target tracking, which have been considered, is exhibited in Figure 21a. In addition, Figure 21b exhibits the considered Simulink to achieve UAV tracking based on TDOA, FDOA and AOA proposed by authors.  
The AEKF is suggested for updating the noise covariance at each estimation state. In this study, the simulation results show a trajectory of the mobile target on Earth. In order to demonstrate the tracking performance of the AEKF algorithm, 100 runs of Monte Carlo simulation results were achieved. The simulation results show considerable optimization for target tracking precision and the algorithm performance comparison with hybrid TDOA/FDOA and TDOA-only measurements. In addition, RMSE has recorded the reduction of about 32.53% and 39.09% in position and velocity, respectively, when employing TDOA/FDOA instead of TDOA individually [112].  
From the Figure 22, we can note the performance improvement of target tracking estimation when we used the AEKM and ACKF compared to other filters.  
Figure 20: Geometry distribution of static sensors and moving targets in the simulation [109].  
Figure 21: Structure and Simulink of algorithms for target tracking estimation (modified into Adaptive Extended Kalman Filter with TDAO/FDOA/AOA measurement). **(a,b)** show the framework of localization and target tracking using A EKF based on TDOA/FDOA measurements and Radar tracking using MatLab Function Block respectively. **(a)**[112], **(b)**[113].  
Figure 22: _Cont_.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Data Source
#### 2.2.2 Optical Remote Sensing Data  
Sentinel-2 data were downloaded from the ESA ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) (accessed on 10 October 2023))). Forest canopy reflectance has significant seasonal variation patterns [22]. Due to the complexity and diversity of forest ecosystems, different forest canopy structures, plant functional traits (such as leaf structure and pigment content), water stress and forest health status all affect their spectral features, and maximum canopy reflectance is a proxy for canopy photosynthetic capacity [23; 24]. Therefore, the maximum reflectance of the forest phenological period was selected as the data for remote sensing inversion. The forest leaf germination period is from late March to late April of each year, and the leaf extension period is from early May to early August of each year. The forest defoliation period is from mid-September to late October in the Loess Plateau. Sentinel-2 L2A product data were selected using Google Earth Engine (GEE) to complete the synthesis of maximum reflectance in the growing season, and the spatial resolution was resampled to 30 m, which is consistent with the spatial scale of field observation data. In this study, Google Earth Engine (GEE) was used to implement Sentinel-2 data preprocessing. The Savitzky-Golay (S-G) filtering algorithm was selected to achieve spectrum smoothing and de-noising in the growing season. We used forest cover data to inundate Sentinel data to obtain forest canopy reflectance.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 4 EOR Screening Modeling
### Results and Discussion
Furthermore, the utilization of the RF algorithm, a well-established and mature classification technique, contributes significantly to the model's stability in operations. Figure 12: ROC curve of the test set based on naïve Bayes classifier. The visualization of the classification results of the random forest classifier for the test set via the ROC curve and the confusion matrix demonstrates the optimized model's high accuracy in predicting EOR techniques. By employing the RF algorithm as the core of the prediction system and storing the acquired drilling data in the dataset, an appropriate EOR technique tailored to the specific reservoir characteristics can be determined. Given that the parameter distribution of the collected dataset spans across a significant portion of reservoir variations, the classification model established using this dataset holds the  
Figure 14: Confusion matrices for the test set (ANNs). Figure 15: Confusion matrices for the test set (naïve Bayes). potential for widespread application and implementation. This approach assists reservoir engineers in decision making, saving time and economic costs by streamlining the process of identifying the most effective techniques for enhanced oil recovery.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design
### Iterative Analog-Digital Equalizer
Because the feedback matrix \(\mathbf{B}_{d,k}^{(i)}\) is independent of the constraints of the optimization problem (16), the digital feedback matrix can be designed by minimizing the \(\text{MSE}_{k}^{(i)}\) as  
\[\mathbf{B}_{d,k}^{(i)}=\text{arg min}\sum\limits_{k=1}^{S}\text{MSE}_{k}^{(i)}. \tag{17}\]  
From the KKT conditions of the previous problem i.e., \(\partial\!\left(\sum\limits_{k=1}^{S}\text{MSE}_{k}^{(i)}\right)\!/\partial \!\left(\mathbf{B}_{d,k}^{(i)}\right)=0\), the solution is  
\[\mathbf{B}_{d,k}^{(i)}=\left(\mathbf{W}_{d,k}^{(i)}{\left(\mathbf{W}_{a}^{(i)} \right)}^{H}\mathbf{H}_{k}-\mathbf{I}_{U}\right)\!\left(\mathbf{\Psi}^{(i-1)} \right)^{H}. \tag{18}\]  
Replacing (18) in (14), the \(\text{MSE}_{k}^{(i)}\) is up to a constant equal to [29]  
\[\overline{\text{MSE}}_{k}^{(i)}=\left\|\!\left(\mathbf{W}_{d,k}^{(i)}{\left( \mathbf{W}_{a}^{(i)}\right)}^{H}-\overline{\mathbf{W}}_{fd,k}^{(i)}\right)\! \left(\overline{\mathbf{R}}_{k}^{(i-1)}\right)^{1/2}\!\right\|_{F}^{2}\!\!\!. \tag{19}\]  
\[\overline{\mathbf{W}}_{fd,k}^{(i)}=\left(\mathbf{I}_{U}\!\left|\!\mathbf{\Psi }^{(i-1)}\right|\!\!\right)\!\mathbf{I}_{k}^{H}\!\left(\overline{\mathbf{R}}_ {k}^{(i-1)}\right)^{-1}\!\!\!, \tag{20}\]  
\[\overline{\mathbf{R}}_{k}^{(i-1)}=\mathbf{H}_{k}(\mathbf{I}_{U}\!\left|\! \mathbf{\Psi}^{(i-1)}\right|\!\!\right)\!\mathbf{I}_{k}^{H}+\sigma_{n}^{2} \sigma_{n}^{-2}\mathbf{I}_{N_{n},} \tag{21}\]  
where \(\overline{\mathbf{W}}_{fd,k}^{(i)}\) denotes the non-normalized full-digital equalizer [29]. Therefore, the analog and digital parts of the feed-forward matrix are the solution of the following optimization problem  
\[ \begin{array}{c}\left(\mathbf{W}_{a}^{(i)},\mathbf{W}_{d,k}^{(i)}\right)= \\ \text{arg min}\sum\limits_{k=1}^{S}\overline{\text{MSE}}_{k}^{(i)}\\ \text{s.t.}\sum\limits_{k=1}^{S}\text{diag}(\mathbf{W}_{d,k}^{(i)}{\left( \mathbf{W}_{a}^{(i)}\right)}^{H}\mathbf{H}_{k})=S\mathbf{I}_{U}.\\ \mathbf{W}_{a}^{(i)}\in\mathcal{W}_{a}\,.\end{array}  \tag{22}\]  
As optimization problem (22) is nonconvex, an optimum solution is difficult to obtain. Nevertheless, in the following section, we propose a method to obtain an approximate solution to this optimization problem.
## 3 Calculation of Loudspeaker Driving Signals  
Each method described above result in one truncated impulse response per loudspeaker and pressed key \(R_{\text{f}}(\mathbf{\Theta},\mathbf{\kappa}_{a},t)\). Here, each impulse response is truncated in frequency. To combine them, the three truncated impulse responses per loudspeaker and pressed key are simply added, i.e.,  
\[R(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},t)=R_{\text{f}1}(\mathbf{\Theta}_{l},\mathbf{\kappa}_{ a},t)+R_{\text{f}1}(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},t)+R_{\text{f}1}(\mathbf{ \[PERSON]}_{l},\mathbf{\kappa}_{a},t). \tag{21}\]  
The result of Equation (21) is a broadband impulse response \(R(\mathbf{\[PERSON]}_{l},\mathbf{\kappa}_{a},t)\) that covers the complete audible frequency range. An example is illustrated in Figure 13.  
Summing the truncated impulse responses yields one broadband impulse response for each loudspeaker and pressed key, i.e., \(B=L\times A=7936\) broadband impulse responses \(R_{\mathbf{\[PERSON]}_{l},\mathbf{\kappa}_{a},t}\). These impulse responses describe how any frequency is radiated by the loudspeaker array to approximate the sound field that the harpsichord would create, if excited by a broadband impulse.  
Naturally, a played harpsichord is not excited by an impulse. Instead, pressing a key creates a driving signal that travels through the string and transfers to the soundboard via the bridge. In Section 2.1 we provide literature that suggests three different ways to record or model this driving signal \(h(\mathbf{\kappa}_{a},t)\). In order to finally use the radiation keyboard as a harpsichord sampler, the loudspeaker driving signals \(d(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},t)\) are calculated by a convolution of the impulse response with the source signal, i.e.,  
\[d(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},t)=h(\mathbf{\kappa}_{a},t)*R(\mathbf{\Theta}_{l}, \mathbf{\kappa}_{a},t). \tag{22}\]  
This yields \(B=7936\) sound files. These are imported to multiple instances of a software sampler in a digital audio workstation (DAW). Typically, one instance of a software sampler can address between 16 and 64 output channels. Consequently, between 2 and 8 sampler instances need to be initialized. Technologies like VST and Direct-X are able to handle this parallelism, and several multi-channel DAWs (like Steinberg Cubase, Ableton Live and Magix Samplitude) can handle the high number of output channels. Finally, the original keyboard of the harpsichord is replaced by a MIDI-Keyboard, whose note-on command triggers the 128 samples for the corresponding note.  
As the effect of key velocity on the created level and timbre is negligibly, the harpsichord is the ideal instrument to start with; only one sample per note and loudspeaker is necessary. For more expressive instruments, such as the piano, the attack velocity affects the produced level and timbre. Here, several samples per note, or one attack-velocity controlled filter would have to be applied. This implies the need for much higher data rates and specific signal processing, which is out of scope of this paper.
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 5 Design and Implementation
### Proxy-Based Architecture  
Asgard and Midgard were developed using a proxy architecture [6], as can be seen in Figure 2. In this architecture, our honeypot system is the proxy system that poses as the SSH server to the attacker and also the SSH client to the real OpenSSH server [27]. As such, all the communication between the attacker and the real system can be captured, logged and modified by the proxy system.  
Even though the usage of a proxy is not new, as it has been used to detect web attacks or to defend web applications by using deceptive techniques [28; 29; 30; 31], it has never been used in the SSH protocol to intercept and control the communication. The proxy-based architecture has several advantages: first, it allows a clear separation between our honeypot and the target system that we want to use as a honeypot. Secondly, the use of the real system behind the proxy allows us to avoid (1) its modification, which is necessary for the HiHP, and (2) the usage of an emulator like in MiHP that provides limited functions. Last but not least, using a container system such as Docker ([[https://www.docker.com/](https://www.docker.com/)]([https://www.docker.com/](https://www.docker.com/)), accessed on 15 April 2022) to run the SSH server can contain the risk of malicious attacks.
###### Abstract  
In this paper, an online to offline (O2O) method based on visible light communication (VLC) is proposed, which is different from the traditional VLC with modulation and demodulation. It is a new VLC with modulation and recognition. We use RGB light emitting diode (RGB-LED) as the transmitter, and use Pulse Width Modulation (PWM) to modulate the signal to make it flicker at high frequency. Therefore, several features are created. At the receiver, the complementary metal-oxide-semiconductor (CMOS) image sensor is applied to our system to capture LED images with stripes. A convolution neural network (CNN) is then introduced in our system as a classifier. By offline training for the classifiers and online recognition of LED-ID, the scheme proposed could improve the speed of LED-ID (the unique identification of each different LED) identification and improve the robustness of the system. This is the first application of CNN in the field of VLC.  
visible light communication (VLC); online to offline (O2O); RGB-LED; convolution neural network (CNN); complementary metal oxide semiconductor (CMOS) +
Footnote †: journal: Applied Physics Letters
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 5 Discussion
### Impact of Our Assumption of Spatial Homogeneity of Soil Moisture and Roughness on VOD Calculation
#### 5.1.1 Impact of Soil Moisture Spatial Homogeneity
Finally, the noisy \(\sigma^{0}_{\text{soil}}\) and \(\sigma^{0}_{\text{tot}}\) and the corresponding reference \(\sigma^{0}_{\text{soil}}\) and \(\sigma^{0}_{\text{tot}}\) were compared, and the bias (noisy \(\sigma^{0}_{\text{soil}}\) minus the reference \(\sigma^{0}_{\text{soil}}\)) and noisy \(\sigma^{0}_{\text{tot}}\) minus the reference \(\sigma^{0}_{\text{tot}}\)) as well as the Root Mean Square Error (RMSE) on \(\sigma^{0}_{\text{soil}}\) and \(\sigma^{0}_{\text{tot}}\) were computed. Results showed that an underestimation of the vegetated plots SSM by 5 vol.% induces an error on \(\sigma^{0}_{\text{soil}}\) by 0.85 dB for the bias and 1.43 dB for the RMSE. Concerning the error on \(\sigma^{0}_{\text{tot}}\), a bias of 0.63 dB and an RMSE of 0.99 dB were observed. Next, a sensitivity analysis was performed to assess the impact of these errors of \(\sigma^{0}_{\text{soil}}\) and \(\sigma^{0}_{\text{tot}}\) on VOD values computed from Equation (3). For each possible couple of reference elements  
Figure 8: Difference between the SSM measured in each vegetated plot (vegetation height \(>\) 15 cm) and the averaged SSM of all bare plots (vegetation height \(<\) 15 cm) in an area of 15 km \(\times\) 15 km. (\(\sigma_{\text{soil}}^{0}\) and \(\sigma_{\text{tot}}^{0}\)) and their associated noisy elements, reference VOD and noisy VOD were computed. Then, the reference VOD values (without error on SSM) and noisy VOD values were compared (Figure 9). Results show that an underestimation of the vegetated plots SSM of 5 vol.% leads to a slight underestimation of VOD of 0.02 (with an RMSE of 0.04). In addition, the impact of 5 vol.% SSM underestimation on VOD estimations was analyzed according to the NDVI for values between 0.4 and 0.8 (Figure 10). The underestimation of VOD as well as the RMSE increases when the NDVI increases. The underestimation on VOD increases from 0.010 for NDVI = 0.4 to 0.035 for NDVI=0.8. Similarly, the RMSE increases from 0.02 for NDVI=0.4 to 0.06 for NDVI=0.8. Accordingly, the assumption about the equivalence between \(\sigma_{\text{soil}}^{0}\) of vegetated plots and that of bare plots in the surrounding 5 km \(\times\) 5 km considered in order to solve the Equation (3) would not considerably impact VOD estimation.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 4 Experimental Setup
### Emd-Sarima Modeling  
Following the steps described in Section 3.1, the original time series are decomposed into components (IMF) and a residual. The four IMFs and one residual of an original domestic cargo series are plotted below in Figure 4. The short-period (or high-frequency) components are extracted in IMF 1 and IMF 2, and the long-period (or low-frequency) components are given in IMF3 and IMF 4. The last component is the residue which shows the trend of the original time series.  
Also, following the steps described in Section 3.2, the IMFs and residual are each modeled with the SARIMA model. The EMD-SARIMA forecasting results are compared with the SARIMA model, the Holt-Winters model, and a naive model. The Holt-Winters model is another widely used time series forecasting model as is discussed in the literature review, and for the naive model, \(x_{t+1}=x_{t}\) is set to serve as a basic benchmarking model to evaluate the overall performance of different models.  
After the forecasting results of each model are obtained, two different measures have been taken to compare the performance of the models: the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE), which are calculated as follows:  
\[MAE=\frac{1}{N}\sum_{t=1}^{N}|x_{i}-\hat{x}_{i}|, \tag{10}\]  
Figure 4: IMFs and residual of original domestic cargo series.  
Figure 3: Original time series.  
\[MAPE=\frac{1}{N}\sum_{t=1}^{N}|\frac{x_{i}-\hat{x}_{i}}{x_{i}}|. \tag{11}\]
# A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs
## 3 System Model and Problem Formulation  
In this section, the problem of placing UAVs to build an aerial relay infrastructure for users scattered on the ground is modeled as a single allocation \(p\)-hub median problem [26]. The advantage of modeling the problem in this way is that we can limit the number of UAVs needed beforehand, which, in turn, can be adjusted depending of the size of the network. Moreover, by doing so, we consider UAV-ground and UAV-UAV channels. When considering these two types of channels, the UAV-aided communications exhibit several unique characteristics when compared to terrestrial communication networks [1]. In addition, we take network coverage at the expense of the overall increase in the link capacity of the network into account.  
The \(p\)-hub median problem is a classic location problem in which the objective is to determine the placement of \(p\) facilities (called medians) in a graph of \(n\) nodes, that minimizes the sum of distances between each demanding node and the nearest median. Hence, the positioning of multiple UAVs as aerial relays can be regarded as a type of location problem if the individual nodes on the ground are seen as the clients (demanding nodes), while the UAVs are treated as the \(p\) medians providing a service (which, in our case, consists of relaying data packets to or from the nodes assigned to them).  
As previously mentioned, perfect user location information is also assumed, and all UAVs must be placed at a fixed altitude \(h\) from the terrain (also defined beforehand). Due to these constraints and conditions, no minimum data link rate requirement is imposed, and the goal is to determine the placement of UAVs in the network so that the total (sum) link capacity is maximized, while every user on the ground is assigned to only one UAV.