esa-sceva/satcom-chunk-collection huggingface dataset:
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
###### Abstract  
The concept of Internet of Things (IoT) has attracted much research attention for the realization of a smart society. However, the radio transmission coverage of the existing IoT solutions is not enough to connect lots of devices deployed over wide areas. Therefore, satellite networks have been considered as one of the most attractive solutions to wide cell coverage of IoT, i.e., global-scaled IoT. In satellite communication, a digital channelizer is one of the most significant parts that support multiple transponders. Owing to their wide coverage, satellite communication systems are more vulnerable to interference than other types of wireless communication systems. In this study, a cognitive interference cancellation using the inherent properties of a digital channelizer is considered. The proposed method detects a subchannel corrupted by interference and omits it. A simple energy detection method and a modified version are proposed for detection of interference. In the modified (i.e., improved) method, the number of required signal blocks to achieve the target detection performance can be reduced, i.e., the detection performance is improved with the same number of blocks, by exploiting the property of the fast Fourier transform (FFT) algorithm. Detection performance such as false alarm and detection probabilities are analyzed, and the validity of the analysis is verified with numerical results. It is also shown that an interference lower than a certain level in the proposed approach does not need to be cancelled.  
detection; interference cancellation; digital channelizer; satellite communication; cognitive communication +
Footnote †: journal: Sensors
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background
### Task Allocation  
The task allocation is illustrated in Figure 3. Upon the description of a sensing task, the MCS platform estimates the energy consumption of the task by considering the number of sensors that are needed to sense the task and the sensing time. This information along with the location of the task is sent to the participants' smart devices.  
The energy consumption of task \(j\) (\(E_{t_{j}}\)) is a function of the power consumed by the task \(j\) (\(P_{t_{j}}\)) during the sensing time of the task \(j\) (\(T_{j}\)) as formulated in Equation (3). Each task utilizes a random combination of up to \(\Phi\) different sensors. The power consumption for task \(j\) using different sensors is calculated as the summation of each sensor power that is needed for task \(j\) as shown in Equation (4) where \(\Phi_{j}\) denotes the number of utilized sensors by task \(j\) and can be defined as \(1\leq\Phi_{j}\leq\Phi\).  
\[E_{t_{j}}=(P_{t_{j}})*T_{j} \tag{3}\]  
\[P_{t_{j}}=\sum_{s=1}^{\Phi_{j}}P_{t_{j}}^{\text{SS}_{s}} \tag{4}\]  
Upon the estimation of the task cost via task settings (i.e., sensors' energy consumption and task duration), the participants are notified of the task description via the MCS application. Receipt of the task description at a user's mobile device triggers the user's bidding for the tasks. The MCS app sends the bid of the user, which consists of the following fields: bid (i.e., sensing cost), the location of the user and the sociability rate in order for the MCS platform to assess the user's willingness prior to user-task matching. The energy modeler is the first component called by the MCS platform immediately after the receipt of user bids. The energy modeler component is explained in the next subsection.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 6 Evaluation
### Evaluation Datasets  
In general, aggregated mobility data can be generated by simulating individual assets, each with its own individual mobility patterns, and calculating the aggregated occupation of each node at the desired time interval. This enables quite sophisticated behaviors that might take into account specific objectives (and the corresponding trajectories) for each driver. In this paper, we assume a common behavior on all the assets, defined by an MC. Note that this mobility behavior fits precisely the assumption of the scheme described in (1). This deliberate choice facilitates a more rigorous assessment of all other prediction schemes. Furthermore, this mobility model allows the application of a Monte Carlo simulation procedure that ends up providing an aggregated mobility behavior that suits our goal of aggregate mobility prediction. The graph used in the simulations corresponds to the center of the city of Madrid, which is characterized by a graph with \(N=2000\) nodes. The result of each simulation is a time series of aggregated occupations in each node.  
Therefore, each time series is fully determined by the shared mobility model of the assets and the number of assets (\(M\)). For each of these two factors, multiple time series of sufficiently large size (\(T_{D}=1000\)) were constructed, where each time series represents the aggregation of \(M\) samples generated following the corresponding model.  
The different employed (possibly non-homogeneous) MC models are characterized by the corresponding transition probability matrices, which must be compatible with the adjacency matrix of the city graph. In particular, the designed models were:  
1. The column-wise uniform transition matrix \(\overline{\Pi}\) (which favors the performance of predictor (16)).
2. A randomly generated stochastic transition matrix \(\Pi_{r}\).
## 5 Proposed Scheme
### Ciphertext Conversion  
With ciphertext \(c\), authorization \(A_{i}\) from the corresponding user \(U_{i}\), the Server can convert the ciphertext \(c\) to a united one \(c^{{}^{\prime}}\). Ciphertext conversion process can be calculated like (17) and (18):  
\[c^{{}^{\prime}}=(m-b,\beta^{{}^{\prime}}) \tag{17}\]  
\[\beta^{{}^{\prime}}=(g^{r_{i}^{{}^{\prime}}}mod\ N^{2},\frac{\beta_{2}\cdot A_ {i}}{g^{s_{t}\tau}}mod\ N^{2}) \tag{18}\]  
The Second part of ciphertext \(c\) and united ciphertext \(c^{{}^{\prime}}\) are actually ciphertexts under BCP algorithm for the random chosen bias value \(b\). By this proxy re-encryption process thesecret key on them are converted to the secret key of KGC \(s_{KGC}\), which complete the fusion of User data. If is needed, this target secret key could be replaced to any other one and the owner of this secret key can manage all training process. In our scheme, this target key for data fusion is temporary set to be the key of Key Generation Center.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 7 Results
### Node Density and Number of Hops in a Network  
To investigate the effect of node density in a network, a varying number of nodes are placed within a 3.927 km, 5 km, and 7.071 km radius of the GW. The simulations are performed with ten randomly generated node location maps, and ten complete up-link cycles are simulated per setup. The results of the simulations can be seen in Figure 7.  
From the simulations, three conclusions can be drawn. Firstly, a higher node density negatively affects the performance, which can directly be attributed to the higher probability of packet collision due to the reduced spatial diversity of concurrent transmissions. The second important note from the results relates to the number of devices in a network. As the number of devices in a network increase, so does the interference in the network. Although nodes attempt not to transmit simultaneously in the network, the additional transmissions on other SFs and the accumulated RF interference, caused by other nodes beyond the reception range, of the receiving node can lead to substantial packet loss.  
Lastly, in the simulation, the number of hops in the network increases as the network radius increases. Due to the inherent nature of a multi-hop network, the number of packets transmitted in the network increases with the number of hops in the network, thereby increasing the collision probability.
## 3 Results and Discussion
### Effect of Different Frequencies
#### 3.1.1 Z-Direction Specimens
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimens (\(i\) = 1-9)** & \(Z^{0}_{0}\_i\) & \(Z^{0.1}_{100}\_i\) & \(Z^{0.1}_{200}\_i\) & \(Z^{0.1}_{300}\_i\) & \(Z^{0.1}_{400}\_i\) & \(Z^{0.1}_{500}\_i\) & \(Z^{0.1}_{600}\_i\) & \(Z^{0.1}_{700}\_i\) & \(Z^{0.1}_{800}\_i\) & \(Z^{0.1}_{900}\_i\) \\ \hline Average tensile strength (MPa) & 23.68 & 26.16 & 27.06 & 28.31 & 30.35 & 31.59 & 33.44 & 34.65 & 36.27 & 38.92 \\ Standard deviation & 1.61 & 1.34 & 1.29 & 1.22 & 1.16 & 1.02 & 0.92 & 0.85 & 0.73 & 0.71 \\ Growth (\%) & - & 10.5 & 14.3 & 17.3 & 28.2 & 33.4 & 41.2 & 46.3 & 53.2 & 64.3 \\ Average plasticity (\%) & 5.7 & 5.95 & 6.1 & 6.3 & 6.65 & 6.85 & 6.95 & 7.14 & 7.4 & 7.65 \\ Standard deviation & 0.36 & 0.30 & 0.28 & 0.26 & 0.26 & 0.24 & 0.22 & 0.18 & 0.16 & 0.14 \\ Growth (\%) & - & 4.4 & 7.0 & 10.5 & 16.7 & 20.2 & 21.9 & 25.3 & 29.8 & 34.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The tensile properties of the Z-direction specimens processed without applied vibration and with different applied vibrations. Figure 4: The effect of applied vibration (0.1 g amplitude with different frequencies) on the tensile properties of Z-direction specimens.
## 2 Materials and Methods
### Experimental Procedures  
Surface and intramuscular EMG signals were collected concurrently. Six sEMG and six iEMG electrodes were used for able-bodied subjects. Three electrodes were placed on the flexor and three on the extensor muscles. The same number of electrodes were used for three of the amputees. In the other three amputees, it was only possible to use five surface electrodes and three to six intramuscular electrodes. Electrodes placement is shown in Figure 1. Intramuscular EMG signals were filtered with an analog bandpass filter of 100-900 Hz and sampled at 8 kHz. Surface EMG signals were filtered at 10-500 Hz and sampled at 8 kHz [25].  
Each subject performed 11 hand motions in each experimental session: hand open, hand close, flex hand, extend hand, promotion, supination, side grip, fine grip, agree and pointer, and rest. Seven experimental sessions separated by 24 h were completed by each subject. For each session, each hand movement was repeated four times with a contraction and relaxation time of 5 s, as shown in Figure 2. Hence, a single session was 400 s long. The sequence of movements was randomized for each session.  
After data collection and during offline data analysis, time drifting was found for the onset and offset duration of individual time periods. Therefore, individual time period labeling was performed with a semi-automatic technique using MATLAB 2016a. In this method, onset time was manually chosen using a cursor on the data and corresponding time periods were stored automatically. Each period was reduced to three seconds by removing the first and last second of data to remove transient parts.  
Figure 1: Electrodes placement for (**a**) able-bodied subject and (**b**) transradial amputee. For intramuscular electromyography (iEMG), six pairs of wire were inserted into the flexor carpi radialis, palmaris longus muscle, flexor digitorum superficialis, extensor carpi radialis longus, extensor digitorum and extensor carpi ulnaris. Six surface EMG (sEMG) electrodes were placed beside wires.
Remote Sens._**2013**, \(5\), 1292-1310; doi:10.3390/rs5031292  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Azimuth-Variant Signal Processing in High-Altitude Platform Passive SAR with Spaceborne/Airborne Transmitter**  
**[PERSON] * and [PERSON]**  
School of Communication & Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-28-6183-0299.  
_Received: 15 January 2013; in revised form: 19 February 2013 / Accepted: 22 February 2013 / Published: 14 March 2013_  
**Abstract:** High-altitude platforms (HAP) or near-space vehicle offers several advantages over current low earth orbit (LEO) satellite and airplane, because HAP is not constrained by orbital mechanics and fuel consumption. These advantages provide potential for some specific remote sensing applications that require persistent monitoring or fast-revisiting frequency. This paper investigates the azimuth-variant signal processing in HAP-borne bistatic synthetic aperture radar (BiSAR) with spaceborne or airborne transmitter for high-resolution remote sensing. The system configuration, azimuth-variant Doppler characteristics and two-dimensional echo spectrum are analyzed. Conceptual system simulation results are also provided. Since the azimuth-variant BiSAR geometry brings a challenge for developing high precision data processing algorithms, we propose an image formation algorithm using equivalent velocity and nonlinear chirp scaling (NCS) to address the azimuth-variant signal processing problem. The proposed algorithm is verified by numerical simulation results.  
**Keywords:** high-altitude platform; near-space vehicle; synthetic aperture radar (SAR); bistatic SAR; azimuth-variant; passive radar; near-space; chirp scaling; azimuth resolution
### Problem Statement  
In an ideal radio channel, there would only be one direct path signal, a flawless recreation of the transmitted signal. On a natural channel, the signal is altered during transmission. Copies of the transmitted signal that have been attenuated, reflected, refracted, and diffracted make up the received signal. Additionally, the channel puts noise into the signal and causes the carrier frequency to shift as the transmitter or receiver moves (Doppler effect). A radio system's performance depends on the radio channel's characteristics. Therefore, understanding these effects on the signal is crucial. As a result, in our work, we consider a MIMO-OFDM system before employing pilot permutation to estimate the channel. This article compares the L.S., MMSE without DFT, and the L.S., MMSE with DFT. A new DFT-LS-WIENER hybrid channel estimation technique is also proposed to improve bit error rate performance.  
The rest of the paper is organized as follows: Section 2 describes the literature survey. Section 3 presents the proposed DFT-LS-WIENER method. Section 4 discusses the comparison of the proposed work. Finally, Section 5 presents the conclusion
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 3 Proposed Anomaly Detection Framework
### Stationary Time Series Anomaly Detection  
After Tri-CAD judges a time series not to be periodic, it employs the Dickey-Fuller test [37] with the \(p\)-value threshold of 0.0005, which is also taken in [44], to check whether a time series is stationary. This is achieved under the null hypothesis that a unit root exists in an autoregressive model of the time series. If the \(p\)-value returned by a test is less than the threshold of 0.0005, the time series is assumed to be stationary. As mentioned earlier, the \(D\) data of the normal dataset are taken for the Dickey-Fuller test.  
When the time series is determined to be stationary, Tri-CAD calculates the means of the data within a large global sliding time window and a small local sliding time window, as shown in Figure 5. The global window and the local window have the same ending time point. Furthermore, the size (say, 100) of the global window is much larger than the size (say, 5) of the local window. For a data point \(x\), the means of its global window and local window are first calculated, which are denoted as \(Gmean_{x}\) and \(Lmean_{x}\), respectively. Then, x's anomaly score \(s_{x}\) is calculated according to (10).  
\[s_{x}=\left|\frac{Gmean_{x}-Lmean_{x}}{Gmean_{x}}\right| \tag{10}\]  
The anomaly scores of the \(D\) data in the normal dataset and their mean and standard deviation are calculated. Afterwards, for a test data point \(x\) with the anomaly score \(s_{x}\), its SAS \(\lambda_{x}\) is calculated according to Equation (9). Similarly, if \(\lambda_{x}\) is increasing and exceeds a pre-specified threshold, the data point \(x\) is regarded as anomalous.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 2 Materials and Methods  
### Study Area  
This study involved the Rome municipality (Figure 1), which has a public space incidence of 3.7% (over an area of 1287.4 km\({}^{2}\))--or approximately 48 km\({}^{2}\)--allocated among equipped green areas, historical archaeological parks, and large urban parks. Moreover, protected natural areas and nature reserves fall partially within the municipal territory.  
The area selection for the surveys was a critical decision, as the study focused on a single tree species. The greater the extent of tree cover, the easier it is to recognize in PlanetScope imagery. Therefore, locations with extensive areas covered exclusively by pine trees were chosen. This selection was based on specific characteristics, such as canopy density and size. As a result, historic villas, some public parks, and other green areas (Table 1) were included in the study, as these spaces offered more uniform coverage of the same tree species. Large historic villas have the highest concentration of _P. pinea_ (Figure 1).  
All sites were selected within the area defined by Ring Road A90, the main road encircling the city of Rome, which is more densely populated and urbanized. Consequently, large green areas extending outside this boundary, such as Castel Fusano pinewood, were excluded.  
Figure 1: Localization of sites for surveys (marked with grey circles the sites and marked in green the perimeter of the areas) and a detail of a surveyed area (Villa Borghese) with its georeferenced point.
## 5 Privacy Analysis  
Following our design goals, we discuss how the proposed FGPQ scheme achieves the privacy preservation of the LBS provider and mobile users.  
The privacy preservation of mobile users: When a mobile user wants to search the POI satisfying the special spatial range and searching content, he or she sends an encrypted request \((E(m_{*}),E_{Q}(\widehat{L}^{\prime}))\) to the fog node \(id_{j}\). In particular, the ciphertext \(E(m_{*})\) is computed as \(E(m_{*})=(g^{ah(m_{*})r_{*}},g^{r_{*}})\). Hence, based on hard problems of the CDH and DL in bilinear pairing (Definitions 2 and 3), the fog node \(id_{j}\) can not obtain any plaintext information from \(E(m_{*})\) without knowing \(r_{*}\) and \(\alpha\). As \(E_{Q}(\widehat{L}^{\prime})\) is a ciphertext of the ASPE algorithm, the privacy-preserving totally depends on the security of the ASPE algorithm. As analysed in [26], once the vector dimension \(d\) satisfies \(d\geq 80\), it is generally very difficult for an attacker to successfully attack the ASPE algorithm. Therefore, under the parameter setting in Section 4.1, the location can be protected from disclosing. In other words, fog nodes can not obtain any plaintext information from the request \((E(m_{*}),E_{Q}(\widehat{L}^{\prime}))\). In addition, even if the value of scalar-product (see Formula (6)) has been obtained, the fog node \(id_{j}\) cannot get the location, since a large random number \(\beta\) has been applied to confuse the real scalar-product value. Note that fog nodes do not collude with the LBS provider in this paper, so they will not directly forward users' encrypted requests to the LBS provider, thereby, the LBS provider will not obtain users' privacy. Consequently, the privacy-preserving of mobile users can be achieved.  
The privacy preservation of the LBS provider: Under the aforementioned scheme, the LBS provider would encrypt all LBS data before outsourcing. Thus, fog nodes can only obtain the encrypted LBS items. Similarly, the fog node \(id_{j}\) can not obtain any plaintext information from the stored content information \(E(m_{i})\) (\(i=1,2,\ldots,k_{j}\)) based on the CDH and DL problems. In addition, according to properties of the ASPE algorithm (Formulas (3) and (4)), even though the fog node \(id_{j}\) manages \(k_{j}\) encrypted LBS items, it can not obtain any plaintext information about locations. Conceivably, the privacy preservation of the LBS provider can be achieved.
## 4 Discussion
### Visual and Clinical Implications  
The visual results confirm that the proposed GLAM-PViT model produces cleaner, sharper images with reduced noise and artifacts. These improvements make the images diagnostically useful, closely resembling high-dose CT scans. The low uncertainties across datasets ensure consistent performance, highlighting the model's robustness for clinical applications.  
While numerous unsupervised denoising models, such as Noise2 Self [31] and Patch2 Self [32], have demonstrated strong performance in self-supervised noise learning, our study followed a supervised approach, leveraging paired LDCT-NDCT data to train a model with explicit noise-to-clean mappings. This allows for more precise noise suppression and structural preservation compared to unsupervised methods, which rely on statistical assumptions about noise. However, exploring hybrid or self-supervised approaches for LDCT denoising remains an interesting avenue for future research, particularly for scenarios where paired training data are scarce.
## 3 Materials and Methods
By processing diverse modalities like network traffic, log files, and behavior patterns with CNNs, the architecture acknowledges the spatial features that hold significance in cybersecurity contexts. This customized feature extraction enhances anomaly detection's precision in identifying spatial irregularities hidden within complex data patterns. In addition, the integration of Transformers in the Cross-modal Learning Module is an innovative approach to capturing cross-modal interactions and dependencies. Transformers were originally designed for sequence-to-sequence tasks but adapting them for cross-modal learning is a novel application. By processing different modalities with dedicated subnetworks and then aggregating shared representations using Transformers, the architecture harnesses the strength of Transformers in capturing contextual and long-range relationships within different types of data. This integration contributes to the architecture's ability to learn complex patterns across modalities. Also, the Dynamic Attention Module introduces innovation by employing GRUs to compute attention weights. While attention mechanisms are common in machine learning, using GRUs for dynamic attention reflects an innovative application. GRUs, being recurrent neural network components, adaptively adjust attention weights based on the current state and input sequence. This dynamic attention mechanism helps the model focus on the most relevant features at each time step, allowing it to adapt to changing data patterns and improving anomaly detection accuracy. Moreover, the application of the Theil-Sen Regressor for anomaly detection is an innovative choice. While the Theil-Sen Regressor is primarily used for linear regression, adapting it as an anomaly detection algorithm shows innovation. By fitting a linear model to the attention vector and calculating residuals, the architecture detects anomalies in a manner that accounts for potential outliers and noise, contributing to robust and accurate anomaly identification. Collectively, the innovation of the CM-DANA methodology lies in its thoughtful combination of these components and algorithms to address the challenges of detecting anomalies across multiple cyber modalities. The details about the specific components are presented in the following subsections.