esa-sceva/satcom-chunk-collection huggingface dataset:
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Evaluation Criteria  
#### 5.2.1 Extended Modularity of Bipartite Networks \(EQ_{b}\)  
[PERSON] et al. [30] extended the modularity of bipartite network proposed by [PERSON] [16] and proposed \(EQ_{b}\), which is suitable for the evaluation of detection results in the overlapping communities of the bipartite network. The formula of \(EQ_{b}\) is shown in (26).  
\[EQ_{b}=\frac{1}{M}\sum_{c\in C}\left(\sum_{i=1}^{|U|}\sum_{j=1}^{|V|}\psi_{i,c }\psi_{j,c}\left(A_{ij}-\frac{d_{i}d_{j}}{M}\right)\right). \tag{26}\]  
We introduced [PERSON]'s modularity in Formula (16), Except for \(\psi_{i,c}\), the meanings of other attributes in \(EQ_{b}\) are consistent with the modularity proposed by [PERSON]. The membership coefficient \(\psi_{i,c}\) represents the proportion of node \(i\) belonging to community \(c\). The membership coefficient \(\psi_{i,c}\) should meet the normalized characteristics, such as \(0\leq\psi_{i,c}\leq 1\) and \(\sum_{c\in C}\psi_{i,c}=1\). For example, there are a total of \(m+n\) edges connected to node \(i\), among which \(m\) edges belong to community \(c\), and \(n\) edges do not belong to community \(c\); then, \(\psi_{i,c}=\frac{m}{m+n}\).
# Microwave Photonic Devices Based on Liquid Crystal on Silicon Technology
## 3 Flat Passband and Narrow Notch Microwave Photonic Filters
In this case, the amplitude of the optical carrier and the two RF modulation sidebands remain unchanged after the LCOS based optical processor. The optical processor phase response profile is designed so that the sum of the two beating terms, which are the left and right sidebands beat with the optical carrier at the photodetector, produces the desired RF signal phase shift. Note that the two RF modulation sidebands retain after optical signal processing. This avoids losing half of the RF information signal, which results in a higher system SNR compared to the previously reported microwave photonic phase shifter based on LCOS technology and SSB modulation. Figure 9a,b shows the LCOS based optical processor amplitude and phase response profiles for realizing a continuous \(-\)180\({}^{\circ}\) to 180\({}^{\circ}\) phase shift with a high SNR performance. The corresponding microwave photonic phase shifter output amplitude and phase responses were measured on a network analyzer and are shown in Figure 9c,d. The phase shifter output RF signal has less than 3 dB changes in amplitude for different phase shifts, and has a phase deviation of less than 5\({}^{\circ}\) over an 11 to 26.5 GHz frequency range. This demonstrates the frequency-independent phase shifting operation. The phase shifter output RF signal power and the noise floor were measured on an electrical spectrum analyzer connected to the photodetector output. The measurement was performed for a 20 GHz input RF signal and 0.2 modulation index. The phase shifter SNR was measured to be 139 dB in a 1 Hz bandwidth. This is around 14 dB higher than that of the previously reported phase shifter based on LCOS technology [50]. Figure 8: The carrier and the RF modulation sidebands at the input of the LCOS based optical processor, together with the LCOS based optical processor amplitude and phase response profile designed for improving the phase shifter signal-to-noise ratio (SNR) performance. Figure 9: (**a**) Amplitude and (**b**) phase response profile of the LCOS based optical processor designed for improving the phase shifter SNR performance, and corresponding measured phase shifter output RF (**c**) amplitude and (**d**) phase response for different phase shifts.
## 4 Evaluation and Results' Discussion
### Performance Analysis  
We compared our MPSI protocol with the related protocol [28] by executing it multiple times in the same environment and calculating the average value. There were three experiments: Experiment 1, Experiment 2, and Experiment 3. In order to ensure the accuracy of the test, we modified the multithreaded computation in the source code of [28] to a single-threaded one. Additionally, we did not measure the time it took to generate encrypted Bloom filters for the clients of both protocols.
## 3 Data and Methods
### Overview of Machine Learning Models  
This study selects Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) as core modeling algorithms based on three key considerations. First, these algorithms are mature models long validated in geological hazard studies, leveraging standardized implementations from open-source frameworks (e.g., scikit-learn, XGBoost). Their efficient computational performance and integrated hyperparameter optimization tools reduce engineering application thresholds and facilitate model deployment. Second, given the limited dataset with only 44 debris flow hazard positive samples, SVM constructs margin-maximizing decision boundaries via kernel mapping in high-dimensional spaces, while RF and XGBoost mitigate overfitting through Bagging ensembles and gradient boosting iterations, respectively. Third, these algorithms represent distinct machine learning paradigms: although SVM lacks native feature importance metrics, it enables contribution quantification via SHAP values, whereas RF and XGBoost directly output importance rankings based on Gini impurity reduction and split gain mechanisms, providing multi-dimensional interpretability for debris flow susceptibility analysis.  
In recent years, ensemble learning strategies have gained popularity in machine learning applications, enhancing predictive performance by fusing the advantages of multiple base models. For instance, a study on seismic risk assessment of reinforced concrete shear walls [72] proposes stacked machine learning (Stacked ML) models integrated with Bayesian optimization (BO), genetic algorithms (GA), and other optimization strategies. This approach provides a new paradigm for structural seismic performance analysis in complex feature spaces by integrating base algorithms with intelligent parameter optimization. While such ensemble optimization demonstrates predictive advantages in high-dimensional scenarios, this study prioritizes classical algorithms like SVM and RF for their interpretability and computational efficiency in transfer learning contexts.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 2 Materials and Methods  
### Overall Workflow  
As shown in Figure 1, compared with traditional 3D imaging, the distributed SAR multi-channel arrangement scheme proposed in the paper can complete HRWS imaging and 3D imaging at the same time.  
The overall flow chart of 3D imaging is shown in Figure 2. In the 3D imaging process of distributed SAR, 2D imaging of multi-channel SAR echo data is realized first; then, image registration, amplitude correction, and phase correction are carried out to achieve strong coherence between multi-channel SAR images; and finally, height focusing is realized, and the 3D distribution of the scene scattering coefficient is obtained through coordinate conversion. However, there is range ambiguity in the imaging process. Aiming to solve the problem that the range ambiguity affects image registration, a general idea of coarse registration-range ambiguity resolution-precise registration is proposed. Firstly, the weight of special points and regions with a high signal-to-interference ratio is strengthened to realize the overall coarse image registration. Then, the external digital elevation model (DEM) is introduced to obtain the prior knowledge of the target position, which is combined with the DBF algorithm to obtain multi-channel range ambiguity resolution. Finally, precise registration is performed.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 6 MKIDs for Security Applications
They propose that an MKID made of high critical temperature superconductor (HCTS) would give the high sensitivity of MKIDs, with the cost and reliability benefits of Stirling coolers. Simulations have been carried out on a proposed design for these HCTS-MKIDs for THz imaging. An electron beam lithography process has been developed for the fabrication of these devices. [PERSON] et al. [149] in Glasgow have investigated the use of atomic layer deposition (ALD) to deposit titanium nitride (TiN) thin films, for use in MKIDs for passive THz imaging. TiN thin films were the material of choice due to their greater than 300 mK critical temperatures, allowing the use of compact cryogenic systems, reducing the overall complexity of the system. They report that for modern security applications, frame rates of 25 Hz and noise effective temperature differences (NEDT) of \(\leq\)0.1 K are desired, and that for a system similar to that reported by [PERSON] et al. [148] a time constant \(\tau\) of \(\leq\)100 \(\upmu\)s and a noise effective power, \(NEP\), of the order of 100s of pW is needed. A test array consisting of 12 pixels, made up of an inductive meander, in series with an interdigitated capacitor, coupled to a 50 \(\Omega\) coplanar waveguide, was designed and fabricated. They describe results from this test array of \(NEP\approx 2.3\times 10^{-15}\) W/\(\upmu\)Hz, and \(\tau\approx 31\upmu\)s. This time constant meets the stated requirement, but the \(NEP\) is higher than the photon noise of the source, defined by the 250 K blackbody temperature of the test source. They suggest that optimization of the pixel design, and reduction in the readout noise is needed to improve this figure. [PERSON] et al. [152] in Groningen are also developing a THz imager for passive, \"stand off\" imaging for security and biomedical applications. They describe an instrument based on an MKID array, cryostat, warm and cold optics, and mechanical scanner. As an initial demonstration of the use of MKIDs for passive screening, they developed a 4 \(\times\) 4 pixel array for the 350 GHz frequency band (860 \(\upmu\)m). An external scanner was used to create full images of 70 \(\times\) 70 pixels with spatial resolution of 3.2 mm of a 220 \(\times\) 220 mm area. This scanning system was then used to take the first THz MKID images of a biological subject, namely, the hand of the author.
## 2 System Description
The direct path between all the devices and AP is completely blocked and channel gain for direct paths is equal to zero. This yields,  
\[h_{eq_{ii}}=\alpha\sum_{r=1}^{N}(|h_{irl}|\times|g_{irl}|)\ \forall\ i\in[1,n],\ l \in[1,m] \tag{2}\]  
For maximizing the amplitude of reflected signal, we put \(\alpha\) in (2) as unity. The \(|h_{eq_{ii}}|^{2}\) is computed as,  
\[|h_{eq_{ii}}|^{2}=\left(\sum_{r=1}^{N}|h_{irl}|\times|g_{irl}|\right)^{2}\forall \ i\in[1,n],\ l\in[1,m] \tag{3}\]  
We assume the Rician fading channel model [12] for channel gain calculation between AP to IRS and IRS to the mobile user. \[h_{irs}=\sqrt{\frac{\kappa}{1+\kappa}}X_{r_{LOS}}+\sqrt{\frac{1}{1+\kappa}}X_ {r_{NLOS}} \tag{4}\]  
Figure 1.0  
The value of \(\kappa\) parameter is taken equal to 3 and \(X_{r_{NLOS}}\) and \(Y_{r_{NLOS}}\) are non-line-of-sight components of the corresponding paths and characterized by Rayleigh distributed random variables with unit variance and zero mean in (4) and (5). \(X_{r_{LOS}}\) and \(Y_{r_{LOS}}\) are line-of-sight components of the corresponding paths and their value is taken as unity in this study. Direct paths from AP to devices are assumed to be missing due to signal blockage or longer distance, and communication occurs only via RIS to devices. A common frequency band is shared among the devices belonging to a NOMA cluster. In this paper, our primary focus is to maximize the IDR when channel gains are already known. Hence, we assumed the perfect channel state information (CSI) and calculated the expression for IDRs. Although, the channel estimation is a complex task for the TDMA-RIS-assisted NOMA system, some channel estimation techniques [19; 20] may be used to estimate the channels from AP-to-RIS and from RIS-to-user devices. The positions of RIS and AP are fixed. The composite signal received at the \(D_{il}\) is given by,  
Figure 1.1  
where \(y_{il}\) is the received signal at \(D_{il}\), \(w_{il}\) represents the additive white Gaussian noise (AWGN) with zero mean and \(\sigma^{2}\) variance for all \(D_{il}\)s. Then the IDR of any user device inside the cluster can be given as,  
Figure 1.2  
where \(R_{il}\) is the individual data-rate (IDR) achieved by the device \(D_{il}\) in the system and \(\gamma_{il}\) is the SINR corresponding to device \(D_{il}\). The SINR of \(i\)th user in a downlink NOMA cluster is given by  
Figure 1.3  
where \(P_{T}\) is total power allocated to the cluster, \(a_{il}\) is the power coefficient of \(i\)th device in the \(l\)th cluster. \(\sigma^{2}\) is the variance of AWGN noise that represents the noise power.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 3 System Model and Performance Analysis
### Average BER Analysis  
The conditional BER for OOK modulation in the presence of AWGN is expressed as  
\[P_{b}(h)=Q(\sqrt{\eta}I) \tag{9}\]  
where \(Q(\cdot)\) denotes the Gaussian \(Q\)-function. The average BER is obtained by averaging over the fading distribution of \(I\):  
\[\overline{P_{b}}=\mathbb{E}_{I}[Q(\sqrt{\eta}I)]=\int_{0}^{1}Q(\sqrt{\eta}I)f( 1)\,dI. \tag{10}\]Using the Chiani approximation for the \(Q\)-function [16],  
\[Q\Big{(}\sqrt{\eta I}\Big{)}\approx\frac{1}{12}e^{-\eta I/2}+\frac{1}{4}e^{-2 \eta I/3}, \tag{11}\]  
the average BER becomes the following:  
\[\overline{P_{b}}\approx\frac{1}{12}\int_{0}^{1}e^{-\eta I/2}f(I)\,dh+\frac{1}{ 4}\int_{0}^{1}e^{-2\eta h/3}f(I)\,dI. \tag{12}\]  
The integral of the exponential function multiplied by the Beta PDF is known in closed form as follows [17]:  
\[\int_{0}^{1}e^{-aI}I^{\alpha-1}(1-I)^{\beta-1}dI=B(\alpha,\beta)\cdot{}_{1}F_{ 1}(\alpha;\alpha+\beta;-a), \tag{13}\]  
where \({}_{1}F_{1}(\cdot)\) is the confluent hypergeometric function of the first kind, defined by the series [18]:  
\[{}_{1}F_{1}(a;b;z)=\sum_{n=0}^{\infty}\frac{(a)_{n}}{(b)_{n}}\frac{z^{n}}{n!}, \tag{14}\]  
with \((a)_{n}\) denoting the Pochhammer symbol, i.e., the rising factorial. Substituting Equation (13) into Equation (12) yields the following:  
\[\overline{P_{b}}\approx\frac{1}{12}\cdot{}_{1}F_{1}\Big{(}\alpha;\alpha+\beta ;-\frac{\eta}{2}\Big{)}+\frac{1}{4}\cdot{}_{1}F_{1}\Big{(}\alpha;\alpha+\beta; -\frac{2\eta}{3}\Big{)}. \tag{15}\]
# Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening
## 4 Discussion  
### Best Model for the Risk Calculator  
The MetS is considered to be a potential risk factor for cardiovascular disease and diabetes, it has grown exponentially in Mexico and other countries. For this reason, the development of risk calculator tools is important, especially within the prevention perspective. Based on the above, researches have developed models applying machine learning algorithms to support the diagnosis or prediction of MetS, while considering diverse definitions, such as NCEP ATP III, WHO, and IDF (three of the most used criteria throughout the world [55]), which consider health parameters that must be determined with blood screening. However, other studies [19; 21; 56] have proved that MetS can also be diagnosed without blood studies, while taking other risk factors into consideration.  
In this work, we used RF, C4.5, and DNN with the purpose of identifying risk factors that are related to anthropometric factors, sleepy manner associated with snoring, and dietary habits to predict MetS. Accordingly, [18] comparing RF with types of ANN is a prominent topic.  
Each algorithm was performed by 30 independent runs, the average of ACC, B.ACC, PPV, and NPV are presented in Table 4 for women and Table 5 for men. The results for both genders shows that highest value in the metrics was obtained by the RF with a mtry = 1 and ntree = 300 for women (ACC = 84.12%, B.ACC = 62.93%, PPV = 84.78%, and NPV = 75.92%) and a mtry = 10 and ntree = 800 for men (ACC = 88.17%, B.ACC = 80.73%, PPV = 92.29%, and NPV = 70.72%).  
Based on the results that are shown in Tables 4 and 5, it is possible to observe that RF and the DNN show comparable performances in ACC and B.ACC, but lower performance  
Figure 4: Best predictive model built with C4.5 for men across 30 independent runs.  
in PPV and high performance in NPV, however the results that are obtained by RF are close to each other, presenting a suitable solution for the prediction of MetS. The optimal model of RF was performed analyzing several models with ntree between 100 to 1000 trees and mtry between 1 to 10.  
Additionally, it is essential to highlight that deep learning models do not require an earlier feature selection step; they get the features from the data; even the above is a property for DL, a better performance was obtained by selecting features by PCC, RF by category, and all characteristics. One test was carried on with all of the features, but this experiment had a more unsatisfactory performance; the main reason is the quantity of data; DL is more effective when the set has a large amount of information.
# Practical Grammar Compression Based on Maximal Repeats +
## 4 MR-RePair
### Naive-MR-RePair
Here \(\hat{R}_{rp}\) and \(\hat{R}_{nmr}\) are defined as follows:  
* Assume that \(x_{i}\in\hat{V}_{rp}\) for \(1\leq i\leq m\) and \(y_{j}\in\hat{V}_{rp}\cup\hat{\Sigma}_{rp}\) for \(1\leq j\leq|w|\), then \(\hat{R}_{rp}\) consists of
* \(|w|\) rules \(y_{j}\to y_{i}y_{r}\) with \(val(y_{|w|})=uw\),
* one rule \(x_{1}\to y_{|w|}y_{|w|}\) and \(\log_{2}\left[2^{m+1}-1\right]-1=m-1\) rules \(x_{i}\to x_{i-1}x_{i-1}\) for \(2\leq i\leq m\), and
* one rule \(\hat{S}_{rp}\to x_{m}x_{m-1}\cdots x_{1}y_{|w|}\). * Assume that \(d=|\hat{V}_{nmr}|=|\hat{R}_{nmr}|\) and \(z_{i}\in\hat{V}_{nmr}\) for \(1\leq i\leq d\), then \(\hat{R}_{nmr}\) consists of
* one rule \(z_{1}\to uwu\), and
* \(d-1\) rules \(z_{i}\to z_{i-1}wz_{i-1}\) for \(2\leq i\leq d\) and \(z_{d}=\hat{S}_{nmr}\). Let \(\hat{g}_{rp}\) and \(\hat{g}_{nmr}\) be the sizes of \(\hat{G}_{rp}\) and \(\hat{G}_{nmr}\), respectively. Then, the following holds:  
\[\hat{g}_{rp} =2|w|+2m+(m+2)=3m+2|w|+2, \tag{4}\] \[\hat{g}_{nmr} =|w|+2+(|w|+2)(d-1)=(|w|+2)d. \tag{5}\]  
Here, with regard to the length of \(T^{\prime}\), we have  
\[n =(2(2^{m}-1)+1)(|w|+1)+1,\text{ and}\] \[n =(2^{d}-1)|w|+2^{d}.\]  
From these, \(d=m+1\) holds. Hence, according to Equations (4) and (5), the following holds:  
\[\hat{g}_{nmr}-\hat{g}_{rp}=(m-1)(|w|-1)-1.\]  
Therefore, \(\hat{g}_{nmr}>\hat{g}_{rp}\) holds for some \((m,|w|)\), and the proposition holds.
## 4 Field Experiments  
### Experimental Facilities  
The field experiments are conducted with Chongqing Jiaotong University's \"XinYuan\" autonomous vehicle, as shown in Figure 4. All of the vehicle's signals are collected by a V-BOX integrated device, a vehicle performance test system provided by the British Racelogic company. As shown in Figure 4, the steering wheel angle and torque is measured by a steering sensor mounted on the steering wheel. The pedal force and string position sensors are responsible for collecting the pedal force and stroke. The vehicle's position and speed information are recorded by the Global Positioning System / Real-time kinematic (GPS/RTK). The vehicle's state, such as \(V_{y}\), \(\omega_{y}\), \(e_{y}\), \(\psi\), can be collected by the IMU inertial sensor.  
In order to identify the parameters of the arm's NMS characteristic, a 16-channel EMG measurement instrument supplied by the OT Bio company in Italy is utilized in the experiment. Its sampling rate, resolution rate and bandwidth are 1024 Hz, 16 bit and 10 Hz-500 Hz, respectively. The Figure 5 illustrates that primary working muscle groups are monitored by EMG electrodes which are placed and oriented according to surface Electromyography for the non-invasive assessment of muscles (SENIAM) standards [30]. The working muscle groups affecting the driver's steering ability are listed in Table 3[31; 32].  
Figure 4: Field experimental facilities.
# Fully Open-Source Meeting Minutes Generation Tool
## 6 Discussion  
Online meetings and online presentations have become essential with the rise of remote work. They often lead participants to over-fatigue due to information overloads and cramped meetings. To address this, the proposed system extracts, summarizes, and correlates the key information from recorded meetings using fully open-source AI models. The system enables users to review essential contents without replaying entire sessions and to update the system whenever a new advancement of an AI model occurs. Following a modular and task-specific approach, each piece of audio, text, and visual data is processed individually to maximize computational efficiency and accuracy.  
The _WER_ by _Whisper_ is very similar to the accuracy of human transcription [14]. Although the _ROUGE-N_ scores, _cosine similarity_, and user feedback obtained in this study indicate slight limitations in generating cohesive summaries and representative keywords in practical settings, their usefulness often depends on how effectively they capture the critical information discussed in the meeting.  
Another limitation of the proposed system is that it was tested with controlled recorded online meeting presentations. These presentations excluded images, figures, and animations and used a single background color with the standard font size and style. The speech pattern was kept uniform, with no variation in speed or intonations.  
Considering these limitations, future investigations will focus on varied presentation formats and speech patterns. More complex scenarios will represent real-world challenges. Additional techniques or model fine-tuning may be necessary to ensure the system can generate usable information from more complex inputs.
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### Introducing Twisting: Lay Length Definition
#### 2.2.1 Helical Transformation  
Figure 3 displays a conceptual 3D model for the five-segment cable with twisting. Mathematically, the center of each segment is rotated about the \(z\)-axis as a function of \(z\) as  
\[\theta(z)\ =\ \frac{2\pi}{\lambda}\,z\,, \tag{1}\]  
where \(\lambda\) is the specified lay length. Each segment's cross-section is swept along this helical path to create the final cable geometry. Within each segment itself, no additional twisting of individual strands is considered (i.e., each segment is internally \"solid\").