esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems
### Heterodyne Coherent Detection Architecture  
Signal detection in terahertz communication systems is divided into two main schemes: direct detection and heterodyne detection. The direct detection scheme amplifies THz signals received by antennas through EAs and then directly down-converts the high-frequency signal to baseband using an envelope detector. It has a simple structure and low cost, and it is insensitive to phase noise, but it only supports demodulation of intensity-modulated signals and suffers from nonlinear distortion and power wastage, leading to limited signal quality, making it suitable for low-speed, low-cost scenarios [109; 110]. Therefore, in long-range wireless terahertz communication systems, we prefer the heterodyne detection approach. The structure of the heterodyne detection scheme in THz communication is shown in Figure 14. This scheme primarily mixes an LO signal with the received signal to generate an IF signal. After digital down-conversion and capture by a digital oscilloscope, phase correction via DSP fully recovers the I/Q components of the vector-modulated signal [111].  
Figure 14: Heterodyne coherent detection schematic diagram.  
Figure 13: Complex environment transmission system based on InP semiconductor amplifier: (**a**) transmitter setup; (**b**) photo of transmitter; (**c**) photo of receiver; (**d**) receiver setup.  
The vector signal received by the THz receiver can be expressed as  
\[S(t)=A[I(t)\sin(2\pi f_{c}t+\theta_{c}(t))+Q(t)\cos(2\pi f_{c}t+\theta_{c}(t))]. \tag{15}\]  
Here, \(A,f_{c}\), and \(\theta_{c}\)(t) denote the amplitude, frequency, and phase of the THz signal, respectively. I(t) and Q(t) represent the in-phase (I) and quadrature (Q) components of the vector signal carried by the millimeter wave. The RF LO signal can be expressed as  
\[S_{LO}(t)=A_{LO}\cos(2\pi f_{LO}t+\theta_{LO}(t)). \tag{16}\]  
where \(A_{LO},f_{LO}\), and \(\theta_{LO}\) represent the amplitude, frequency and phase of the RF LO signal, respectively. The THz signal is down-converted to a lower IF bandwidth via mixing with the LO signal, facilitating subsequent signal conditioning. The resulting IF signal after mixing is  
\[S_{IF}(t)=-jA_{LO}A[I(t)+jQ(t)]\exp(j2\pi f_{IF}t+j\theta_{IF}(t)). \tag{17}\]  
The final output is obtained after digital down-conversion processing:  
\[\text{Output}=j\cdot(-j)A_{LO}A[I(t)+jQ(t)]=A_{LO}A[I(t)+jQ(t)]. \tag{18}\]  
Although this scheme requires an LO and mixer--leading to increased system complexity and cost--it supports high-order modulation formats and enables distortion cancellation through DSP algorithms, significantly enhancing receiver sensitivity and spectral efficiency. Consequently, it serves as a core technology for long-distance terahertz communications.
## 4 Mathematical Modeling for Oil Characterization  
The mathematical model for the measured data is obtained using the curve fitting technique. To achieve the best fitting curve, the technique of least squares is used. According to this technique, the sum of deviation must be minimum for a best fitting curve [49]:  
\[\sum_{1}^{n}e_{i}^{2}=\sum_{1}^{n}[y_{i}-f(x_{i})]^{2}, \tag{5}\]  
where \(e_{i}\) is the deviation, \(x\) is the independent variable and \(y\) is the dependent variable. The least error (\(E\)) for the best fitting polynomial \(y=a_{1}+a_{2}x+a_{3}x^{2}\) is:  
\[E=\sum_{1}^{n}e_{i}^{2}=\sum_{1}^{n}\left[y_{i}-\left(a_{1}+a_{2}x_{i}+a_{3}x_ {i}^{2}\right)\right]^{2}, \tag{6}\]  
For \(E\) to be minimum, the following conditions are compulsory: \(\frac{\partial E}{\partial a}=0\), \(\frac{\partial E}{\partial b}=0\), and \(\frac{\partial E}{\partial c}=0\). By applying these conditions to Equation (6), the unknown coefficients (\(a_{1}\), \(a_{2}\), and \(a_{3}\)) for the best fitted polynomial are calculated in the form of the following matrix:  
\[\begin{bmatrix}a_{1}\\ a_{2}\\ a_{3}\end{bmatrix}=\begin{bmatrix}n&\sum x_{i}&\sum x_{i}^{2}\\ \sum x_{i}&\sum x_{i}^{2}&\sum x_{i}^{3}\\ \sum x_{i}^{2}&\sum x_{i}^{3}&\sum x_{i}^{3}\\ \end{bmatrix}^{-1}\begin{bmatrix}\sum y_{i}\\ \sum x_{i}y_{i}\\ \sum x_{i}^{2}y_{i}\\ \end{bmatrix}, \tag{7}\]  
The best fitting polynomial for the complementary meteresonator sensor can be expressed as:  
\[f_{i}=a_{1}+a_{2}\epsilon_{r}+a_{3}\epsilon_{r}^{2}, \tag{8}\]  
where \(f_{i}\) is the dependent variable and \(\epsilon_{r}\) is the independent variable. For the first and second resonance of the fabricated complementary meteresonator sensor, the unknown coefficients (\(a_{1}\), \(a_{2}\), and \(a_{3}\)) are calculated using matrix (7), and the results given in Table 3. The equations for \(f_{1}\) and \(f_{2}\) are given as:  
\[f_{1}=7.362-0.221\epsilon_{r}-0.007\epsilon_{r}^{2}, \tag{9}\]  
\[f_{2}=9.234-1.079\epsilon_{r}-0.0005\epsilon_{r}^{2}. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Oil Under Test (OUT)** & **Dielectric Constant** & **Sensitivity of First Resonance (\%)** & **Sensitivity of Second Resonance (\%)** \\ \hline Corn Oil & 2.526 & 31.4 & 46.5 \\ Coconut Oil & 2.83 & 30.6 & 44.2 \\ Olive Oil & 3.352 & 29.7 & 43.3 \\ Castor Oil & 4.47 & 27 & 39.4 \\ \hline \end{tabular}
\end{table}
Table 4: Relative sensitivity of the complementary meteresonator sensor with respect to OUT.  
Equations (9) and (10) are used to calculated to the resonance frequencies of the complementary metaresonator sensor due to interaction with the OUT and tabulated in Table 5. The measured and formulated results are very close to each other. The complementary metaresonator sensor is compared for sensitivity and application with others available sensors in the literature. The sensitivity of proposed complementary metaresonator sensor is high as compared to other state-of-the-art sensors, as tabulated in Table 6.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Aggregation Routing Protocols for Energy Harvesting WSN
#### 5.2.1 Cluster-Based Routing Protocol That Relies on EH
The second mechanism, termed cluster-head duty-cycle, regulates the frequency at which a node can become a CH based on duty-cycling that adapts to the node's energy-harvesting rate. To conserve the energy of nodes that are more susceptible to energy depletion, [PERSON] et al. (2017) select CHs, taking into account their residual energy, expected harvest energy, distance from the sink, number of neighbors, and, similar to LEACH, the number of times a node has already served as a CH in the past. The proposed approach, which combines centralized and distributed mechanisms, relies on a signal transmitted by the sink that can be received by all the nodes, which allows them to estimate their geographic distance from the sink. The network is partitioned by the sink into four layers based on distance. The sink further computes the individual coverage radius of each node (potential CH) based on distance (nodes closer to the sink, which are therefore expected to relay traffic coming from more distant clusters, will have a smaller radius, and thus less intra-cluster traffic; on the other hand, nodes farther away from the sink, which are therefore expected to have less inter-cluster traffic to relay, are assigned a longer radius, and therefore more intra-cluster traffic). The paper suggests a distributed contention-based mechanism for selecting the CHs in which the contention window takes into account the parameters mentioned above. Another cluster-based routing for EH-WSN is proposed by [PERSON] and [PERSON] (2018). The proposed routing scheme is divided into cluster establishment and data collection. It is suggested that besides the typical cluster members (CMs) and cluster-head (CH), a new entity be devised, termed in the paper scheduling node (SN), which is different from the CH. The main task of the SN is to monitor the energy of all the cluster members during the data collection stage and select a CH based on the monitored residual energy of the cluster members. The transmission range of nodes can also be adjusted based on their residual energy. The data collection stage, which adapts a round-based scheme similar to LEACH, is divided into the data transmission and CH selection stages. [PERSON] et al. (2018) aim to improve the network efficiency by three means: (i) clusterization mechanism that takes into account energy consumption during the data aggregation phase, (ii) duty cycle adaptation of each node such that each sensor node determines its mode of operation, and (iii) routing mechanism based on ant colony optimization that chooses the path between the source and the sink node that reduces the delay incurred. Overall, while the paper jointly addresses several topics in WSN, including those connected to energy-efficient data gathering, its main contribution is the detailed NS3 simulation.
## 2 Materials and Methods
### UPLC/QqQ-MS/MS Quantitative Analysis  
A targeted metabolomics method, an ultrahigh performance liquid chromatography method coupled with triple quadrupole mass spectrometry (UPLC/QqQ-MS/MS) for quantification of flavan-3-ols and procyanidins, was performed according to the procedure described [18].  
The separation was performed with an Exion LC system manufactured by AB Sciex LLC (Framingham, MA, USA) using an Acquity UPLC HS5 T3 C18 (1.7 \(\upmu\)m, 2.1 mm \(\times\) 100 mm) column (Waters corporation, Milford, MA, USA) at 40 \({}^{\circ}\)C. The injection volume was 2 \(\upmu\)L. The mobile phase consisted of water + 0.1% formic acid (A) and acetonitrile + 0.1% formic acid (B). An AB Sciex QTRAP 6500+ (Framingham, MA, USA) was operated in the negative ion multiple reaction monitoring (MRM) mode using a Turbo V ion source with the following settings: Curtain Gas (CR) 35 \({}^{\circ}\)C, IonSpray Voltage (IV) \(-\)4500 V, Temperature 400 \({}^{\circ}\)C, Collision Gas (CAD) Medium, Ion Source Gas 1 (GS1) 55 psi, and Ion Source Gas 2 (GS2) 45 psi. MultiQuant and Analyst from AB Sciex LLC (Framingham, MA, USA) were used for data acquisition and processing, respectively.  
Lyophilized SC and PH decoctions as well as extracts obtained earlier and described in previous article [10]: 25% EtOH, 40% EtOH and 60% acetone were prepared for quantitative analysis as described below. In total, 10 mg of each sample was dissolved in 2 mL of 80% MeOH in a volumetric flask and then filtered through a 0.22 \(\upmu\)m PVDF syringe membrane (Millipore Millex-GV, Milipore, Burlington, MA, USA) to vials. Samples were also tested after 20-fold dilution. For each sample the procedure was repeated at least once.
## 2 Materials and Methods
### Databases
#### 2.2.4 Topographic Data  
The Shuttle Radar Topography Mission [38; 39] provides a dataset of 30 m resolution grid cells as the basis for the digital elevation model (DEM). SRTM mapped Earth's topography between 56 degrees south and 60 degrees north of the equator. SRTM has a vertical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Taxonomical Unit of Major Agricultural Soils** & **No. of Parcels Covered** & **Area (ha)** & **Area (\%)** \\ \hline Hungarian classification & WRB 2014 & & \\ Lessivated brown forest soil (non-podzolic) & _Haplic Luvisol_ & 11,062 & 385,048 & 10.06 \\ Raman-type brown forest soil & _Haplic Cambisol_ & 6567 & 270,239 & 7.06 \\ Rust-brown sandy forest soil & _Arenic Cambisol_ & 2988 & 114,872 & 3 \\ Typical calcareous cherrozem & _Haplic Cherrozems_ & 3792 & 228,240 & 5.96 \\ Great Plains calcareous cherrozem & _Haplic Cherrozems_ & 2042 & 120,123 & 3.14 \\ Carbonated meadow cherrozem & _Glevic Cherrozems_ & 5540 & 330,200 & 8.63 \\ Non-carbonated meadow chernozem & _Luvic Cherrozems_ & 2021 & 108,149 & 2.83 \\ Carbonated meadow soil & _Calcic Vertisols_ & 3952 & 184,853 & 4.83 \\ Non-carbonated meadow soil & _Haplic Vertisols_ & 3460 & 151,394 & 3.96 \\ Carbonated alluvial meadow soil & _Glevic Fluisols_ & 3129 & 142,535 & 3.73 \\ Non-carbonated alluvial meadow soil & _District Fluisolsols_ & 4658 & 179,101 & 4.68 \\ Carbonated humic alluvial soil & _Calaric Fluvisols_ & 1210 & 51,720 & 1.35 \\ Non-carbonated humic alluvial soil & _Dystic Fluvisols_ & 1584 & 50,789 & 1.33 \\ Carbonated humic sandy soil & _Calaric Cambisols_ & 3714 & 138,044 & 3.61 \\ Non-carbonated humic sandy soil & _Dystic Cambisols_ & 2458 & 75,656 & 1.98 \\ major soils in total & & 58,177 & 2,530,963 & 66.2 \\ other soils & & 28,517 & 1,295,467 & 33.8 \\ \(\Sigma\) & & 86,695 & 3,826,430 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of the AIIR dataset, based on Hungarian [32] and World Reference Base for Soil Resources [33] classification.  
accuracy of 5.3 m (RMSE) in Hungary [40]. The SRTM-derived DEM was used to include a topographic component to the land evaluation model.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 6 Results  
This section presents the results achieved when comparing the transmission power to observe the cumulative distribution function (CDF) and OFDM along with the PAPR. The CDF graphs consist of different BER values on the abscissa axis (Xo in the figure) and the probabilities of obtaining them displayed at the ordinate axis (P(BER\(<\)Xo)).
## 3 Results
### Shadow Detection Combining Topography and Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Image** & **Method** & \(P_{s}\)**(\%) & \(P_{n}\)**(\%) & \(U_{s}\)**(\%) & \(U_{n}\)**(\%) & \(E_{c}\)**(\%) & \(E_{o}\)**(\%) & \(\tau\)**(\%) & \(kappa\) \\ \hline \multirow{8}{*}{7} & [PERSON] & 89.38 & 26.48 & 54.29 & 71.84 & 73.52 & 10.62 & 57.56 & 0.16 \\  & [PERSON] & 97.86 & 19.37 & 54.25 & 90.26 & 80.63 & 2.14 & 58.15 & 0.17 \\  & [PERSON] & 90.12 & 37.24 & 58.38 & 79.41 & 62.76 & 9.88 & 63.37 & 0.27 \\  & [PERSON] & 94.79 & 41.49 & 61.28 & 89.07 & 58.51 & 5.21 & 67.83 & 0.36 \\  & ECA & 97.49 & 84.55 & 86.04 & 97.18 & 15.45 & 2.51 & 90.94 & 0.82 \\  & Ours (MC3) & 95.67 & 68.48 & 74.78 & 94.18 & 31.52 & 4.33 & 81.92 & 0.64 \\  & Ours (CTS) & 91.41 & **96.41** & **96.13** & 91.99 & **3.59** & 8.59 & **93.94** & **0.88** \\ \hline \multirow{8}{*}{8} & [PERSON] & 99.27 & 63.43 & 73.68 & 98.83 & 36.57 & 0.73 & 81.63 & 0.63 \\  & [PERSON] & 98.70 & 61.22 & 72.41 & 97.86 & 38.79 & 1.30 & 80.25 & 0.60 \\  & [PERSON] & 98.85 & 69.65 & 77.06 & 98.33 & 30.35 & 1.15 & 84.48 & 0.69 \\ \cline{1-1}  & [PERSON] & 96.32 & 83.71 & 85.91 & 95.66 & 16.29 & 3.68 & 90.11 & 0.8 \\ \cline{1-1}  & ECA & 92.86 & 89.72 & 90.30 & 92.42 & 10.28 & 7.14 & 91.31 & 0.83 \\ \cline{1-1}  & Ours (MC3) & 98.42 & 72.18 & 78.49 & 97.80 & 27.82 & 1.58 & 85.50 & 0.71 \\ \cline{1-1}  & Ours (CTS) & 95.86 & **89.74** & **90.59** & 95.45 & **10.26** & 4.14 & **92.84** & **0.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative evaluation results of shadow detection. The CTS method, by analysis of the DEM, is able to correctly identify shadows cast on the ground by upland targets without confusion between shaded and non-shaded dark features due to spectral similarity. As shown in Figure 13b, the shadow regions in the rough shadow mask are all approximately accurate and do not contain vegetation and water bodies. Therefore, the vegetation and water body regions have almost no superpixels containing shadow pixels in the rough shadow mask. As shown in Figure 13c, the SPM has almost zero value for the background regions and the highest value for the shadow regions. However, in Figure 13e, the values of the vegetation and water body superpixels are close to the shadow values in the MC3 mean map. After the SPM is fused with the MC3 mean map with certain weights, as shown in Figure 13f, the values of the background superpixels in the JSM are pulled down so that the difference between the shadow superpixels and the vegetation and water body superpixels is enlarged. The addition of the DEM rough shadow mask widens the difference between shadows and dark features. Therefore, applying the multi-level Otsu method for a histogram threshold segmentation of the JSM can delineate shadows and the background more accurately.
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 5 Results
### Comparison of Models
The results obtained for the different series for each model are presented in Figure 10. Figures 11 and 12, respectively, illustrate the box plots for the predictions of the _Path-scan_ model associated with the observed Markov model and the three-state model using the number of packets exchanged per minute. The results of the second scenario indicate that both models exhibit comparable sensitivities for detecting normal behaviors, as observed in the first scenario. However, considering the injection of attacks such as brute force attacks and other types of attacks, our three-state model slightly outperforms the observed two-state Markov model combined with the _Path-scan_ method for this specific task. Indeed, our three-state model shows a false negative rate ranging from 0.02% to 0.22%, whereas the observed two-state Markov model exhibits a false negative rate between 13% and 14%. These results suggest that the three-state model is more effective in detecting abnormal behaviors, with a lower likelihood of missing these behaviors (false negatives). This superiority can be attributed to the three-state model's ability to capture greater variability in data patterns, enabling it to better distinguish between normal and abnormal behaviors even in environments with highly active attacks. These initial results indicate that the three-state model using the number of packets exchanged per minute demonstrates similar or even superior capability in detecting abnormal behaviors compared to the _Path-scan_ model associated with the observed Markov model.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 1 Introduction
[22] proposed a 3D pose visual servoing, which was then illustrated and validated on a cable-driven parallel robot prototype. Although previous research has been done to study the position accuracy problem of cable-driven manipulators, there are few studies which analyze the influence factors that affect the tip positioning accuracy of the CDSM. In this paper, we will establish the kinematic model of CDSMs and analyze the influence of the cable hole location error and cable length error, in order to improve the tip positioning accuracy of the manipulators. The rest of this paper is organized as follows: In Section 2, the structure sketch of the \(n\)-DOF planar CDSM is presented. We firstly derive the kinematics model, and then analyze the influence factors of the tip positioning accuracy and propose a corresponding study method. In Section 3, simulation analysis is made on the influence factors that affect the tip positioning accuracy. Then, we make a demonstrative experiment using a CDSM prototype with 3 sections and 6 driving cables to verify the simulation. In Section 4, we make a conclusion about our work and introduce the future plans.
## 3 Deliberate Attacks on IECPS
### Cyberattacks  
The effective coordination of energy subsystems is intrinsically dependent on the precise control of the cyber system. Numerous sensors and monitoring devices on the physical side transmit system measurement, topology information, and environmental data via a communication network using technologies such as microwave, global positioning system (GPS), and power carrier waves. Upon receiving these data, the control center employs online simulation software to conduct real-time simulations of operational data  
Figure 2: Typical IECPS attacks and intrusion locations.  
and system status, thereby enabling the formulation of appropriate scheduling plans. Once the scheduling plan is finalized, the corresponding operational instructions are transmitted to the control unit via the communication network, which then initiates the necessary adjustments to the system's state upon receiving these instructions. During this process, both the integrity and real-time nature of the data significantly influence the accuracy of control. Attackers compromise data integrity by rewriting, deleting, or adding data [15], and disrupt real-time data transmission by blocking or interrupting communication links [16]. These actions aim to interfere with the system's normal operation. Common forms of cyberattacks include replay attacks (RAs), man-in-the-middle (MITM) attacks, false data injection attacks (FDIAs), time synchronization attacks (TSAs), denial of service (DoS) attacks, and coordinated cyberattacks (CCAs).
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 4 Filter Pruning Based on Feature Maps Clustering
### HCA Method  
The HCA (hierarchical clustering) method can divide the dataset into classes hierarchically, and the class generated later is based on the result of the former layer. Hierarchical clustering algorithms are generally divided into two types. Bottom-up Hierarchical Clustering [32]: each sample is considered as a class at the beginning, and each time, the two closest classes are merged into a new class according to certain criteria. According to this rule, until the end, all samples belong to one class. Top-down Hierarchical Clustering [32]: at first, all samples belong to one class and each class is divided into several classes according to certain criteria. According to this rule, until the end, each sample is a class.  
In this thesis, a bottom-up clustering method is adopted. The process is shown in Figure 8. Suppose there are N samples to be clustered, the specific steps are as follows:  
1. Classify each sample into a class and calculate the distance between every two classes, in other words, the similarity between different samples. In this thesis, the class-average method is used to measure the similarity between two classes, which is the average of the distance between two points in the two classes. For any two classes  
Figure 7: An example for use of SC Method: (**a**) the relationship between cost function and cluster number; (**b**) the relationship between silhouette coefficient and cluster number.  
\(C_{x}\), \(C_{y}\), their similarity is recorded as \(L\big{(}C_{x},C_{y}\big{)}\), and it can be calculated with the following formula:  
\[L\big{(}C_{x},C_{y}\big{)}=\frac{1}{n_{x}n_{y}}\sum_{i=1}^{n_{x}}\sum_{j=1}^{n_{ y}}dist\big{(}d_{xi},d_{yi}\big{)} \tag{4}\]  
where \(n_{x}\), \(n_{y}\) represent the number of samples in the two classes, respectively, \(d_{xi},d_{yi}\) represent the samples in two classes. \(dist\big{(}d_{xi},d_{yi}\big{)}\) represents the Euclidean distance between two samples:  
\[dist\big{(}d_{xi},d_{yi}\big{)}=\parallel d_{xi}-d_{yi}\parallel^{2} \tag{5}\]  
2. Set a threshold to find two classes with closet distance among all the classes and the distance must be smaller than the threshold. If they exist, classify them into one class and reduce the total number of classes by 1. Otherwise, the classification process stops.
3. Recalculate the similarity between the generated new classes and the previous old classes.
4. Repeat steps 2 and 3 until all samples fall into one class, or when the distance between the two closet classes is greater than the specific threshold and the classification stops.