esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Community Detection Based on Small Cliques
## 4 Experiments
### Synthetic Graphs  
The LFR benchmark is widely used for benchmarking community detection algorithms, as it can be used to generate a wide variety of graphs with ground truth communities [2; 4; 8]. We briefly describe the LFR benchmark here, for details we refer the reader to the original publications [11; 12].  
The LFR benchmark generates graphs with \(n\) nodes, a degree power law distribution according to specified average and maximum degree and degree exponent as well as a power law community size distribution with specified minimum and maximum community size and community size exponent. For overlapping communities, \(O_{m}\) specifies the number of communities every node is part of. Out of all edges of a node \(u\), \((1-\mu_{t})\deg(u)/O_{m}\) edges connect it with nodes in its community, while it is connected to \(\mu_{t}\deg(u)\) nodes that are outside of its community. Similarly, \(\mu_{w}\) defines that \((1-\mu_{w})\deg_{w}(u)/O_{m}\) edge weight is distributed over intra-community edges of \(u\) and \(\mu_{w}\deg_{w}(u)\) edge weight is distributed over inter-community edges of \(u\). Hence, \(\mu_{t}\) is referred to as the topological mixing parameter and \(\mu_{w}\) as the weight mixing parameter. If \(\mu_{t}=\mu_{w}\), the edge weight is on average 1.0 for intra- and inter-community edges. In the case of unweighted graphs, we will frequently just use \(\mu\) to refer to \(\mu_{t}\) Every node is assigned to \(O_{m}\) different communities at random such that its internal degree is smaller than the community size. We summarize the parameters and our chosen values in Table 1.  
For unweighted, disjoint communities we use the implementation of the LFR benchmark in NetworKit. For the weighted and overlapping variants we use the implementations provided by the authors of the LFR benchmark.
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.2 Comparative Experiments on Feature Pyramid Architectures  
To evaluate performance discrepancies among feature pyramid architectures for pulmonary organoid detection, we benchmarked BiMAFPN, EMBSFPN, and the Bi-directional Feature Pyramid Network (BiFPN) based on YOLOv11n. As detailed in Table 7, all three architectures enhanced detection accuracy, with BiFPN achieving optimal performance without additional computational overhead.  
Figure 8: Comparison of practical detection effectiveness across models.  
Figure 7: Detection performance, computational cost, and parameter count for each model.
## 3 Methodological Approach
### Input Data and Recommendation  
A squared input matrix collecting linguistic evaluations of influence between pairs of factors is defined according to the opinion provided by an expert in the field of the decision-making problem. Subsequently, these evaluations are translated to Trapezoidal Fuzzy Numbers (TFNs) in the format (a, b, c, d) and collected in the fuzzy input matrix. Each TFN within the fuzzy input matrix is systematically processed using a function defined as a replacement function, which is iterated to extract specific parameters (a, b, c, d) from the fuzzy scale and transform the TFNs into parameterized representations. This method returns the extracted fuzzy parameters corresponding to the linguistic evaluations of input (second table in Section 4.1), thus enabling a more precise characterization of each evaluation. Subsequently, the defuzzification process takes place via the definition of defuzzification. This function is iterated by computing the centroid of gravity of each TFN, as shown in [43; 44]. The outcome of this operation is the derivation of crisp values, effectively replacing the initial TFNs. As an outcome, crisp values are collected in the crisp input matrix, representing the TFNs' defuzzified versions, wherein each entry embodies a crisp value rather than a TFN. This transformation enhances the data's interpretability and facilitates further analyses for the calculation, for each factor, of the related Indirect Effects (IE) and Total Effects (TE).  
Factors with associated higher TE hold practical significance as they refer to a more significant influence or impact within the network. Decision makers often prioritize actions or interventions related to these influential factors, as they have the potential to yield more significant outcomes or changes within the system. The developed system performs a data analysis and recommendation process. It first ranks factors based on their TE values, calculated from the FCM application. Then, it associates specific management measures to each of the analyzed factors, simultaneously identifying the measures with the associated higher priority of implementation. Finally, it generates and prints out recommendations focusing on the measures related to the most crucial elements. This approach helps prioritize actions for safety improvement based on their impact.
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 2 Materials and Methods
### Implementation of SVD Filter with Low Complexity  
Considering the high dimension of \(\mathbf{P}\), directly applying the SVD is time-consuming and not suitable for real-time use. Analogous to the ultrafast time series, the CPWC acquisitions present a lot more spatial points (typically several 10,000) than angles (less than 100) in most cases. Thus, it can be less demanding to get the \(n_{a}\times n_{a}\) covariance matrix as shown in Figure 5 and diagonalize it from the point of view of computing. One should also notice that \(\mathbf{U}\) and \(\mathbf{V}\) also correspond to the eigenvectors of the respective covariance matrices \(\mathbf{P}\mathbf{P}^{\mathrm{H}}\) and \(\mathbf{P}^{\mathrm{H}}\mathbf{P}\). Thus, this process can successfully reduce the \(n_{x}\times n_{z}\) realizations of mainlobe spatial signals to a much smaller scale and get the angular eigenvectors that are also the right singular vector \(\mathbf{V}_{i}\) of \(\mathbf{P}\). The weighted spatial vector \(\lambda_{i}\mathbf{U}_{i}\) can be computed by the projection \(\mathbf{PV}\) then as Equation (6):  
\[\mathbf{P}^{\mathrm{f}}=\mathbf{PV}^{\mathrm{f}}\mathbf{V}^{\mathrm{H}}=\mathbf{U}\Delta^{ \mathrm{f}}\mathbf{V}^{\mathrm{H}}, \tag{6}\]  
where \(\mathbf{P}^{\mathrm{f}}\) is the filtered dataset and \(\mathbf{I}\) is a matrix filter defined by \(\beta\), namely, a diagonal identity matrix with zeros for the last diagonal elements, leading to a new diagonal matrix \(\Delta^{\mathrm{f}}\) of singular values corresponding to the removal of the sidelobe and noise components. In this way, the computation load of the SVD filter is kept low and suitable for real-time imaging.  
Figure 4: The graphical explanation of the proposed singular value decomposition (SVD) filter (\(\mathbf{N}\) represents the rank of matrix \(\mathbf{P}\)).  
Figure 5: The covariance matrix of dimension \(n_{a}\times n_{a}\) is presented here.
## 4 Results and Discussion
### Attenuation Behavior of Non-Ferrous Alloys
In the L direction, three models were compared to the observed spectrum as follows:  
\[\alpha\;(\mathrm{dB/m})=4.53\;\mathrm{f}^{2},\qquad(\mathrm{R}^{2}=0.985) \tag{22a}\] \[\alpha\;(\mathrm{dB/m})=7.84\;\mathrm{f}+3.90\;\mathrm{f}^{2},\quad(\mathrm{R} ^{2}=0.977)\] (22b) \[\alpha\;(\mathrm{dB/m})=1.57\;\mathrm{f}^{2.5}.\qquad(\mathrm{R} ^{2}=0.993) \tag{22c}\]  
The first model gave a good fit according to the R\({}^{2}\) values, while the f\({}^{2.5}\)-dependence provided the best fit. In the T direction, model equations are  
\[\alpha\;(\mathrm{dB/m})=6.73\;\mathrm{f}^{2},\;(\mathrm{R}^{2}=0.985) \tag{23a}\] \[\alpha\;(\mathrm{dB/m})=13.18\;\mathrm{f}+5.00\;\mathrm{f}^{2},\;(\mathrm{R} ^{2}=0.973)\] (23b) \[\alpha\;(\mathrm{dB/m})=13.52\;\mathrm{f}^{1.677}.\;(\mathrm{R} ^{2}=0.995) \tag{23c}\]  
As in the L direction, the f\({}^{2}\)-dependence provided a good (not the best) fit, which was chosen for correlation with dislocation damping. The best-fitting power law curves (Equations (22c) and (23c)) were not selected, since no theoretical basis exists. The observed attenuation curves with fitted quadratic (f\({}^{2}\)) models of Equations (22a) and (23a) are shown in Figure 14b, using squared frequency as the abscissa (and using the linear regression to find constants for Equations (22a) and (23a)). This quadratic behavior may be due to the KGL theory, but the dominance of the linear behavior makes it difficult to assign the KGL-based spectra to a small number of cases. Attenuation followed the usual Mason-McSkimin relation in the thickness (S) direction. Since this Cu plate was finished by cold-rolling processes with an increased Vickers hardness, CW effects caused the anisotropic attenuation with quadratic spectra for the in-plane directions. There were two other cases of lightly cold-worked 70-30
## 3 Results and Discussion
### Bioactivity Study  
The bioactivity test was carried out to obtain information on the ability of the synthesized materials to allow hydroxyapatite formation on their surfaces after being soaked in SBF. This is an important feature to know, as it is strictly correlated with the first step of osteoarthritis [52; 53]. In our case, hydroxyapatite formation was followed by FTIR signals by focusing on the 1100-400 cm\({}^{-1}\) region, and SEM images, both acquired after 21 days of soaking (as shown in Figure 8). It has already been demonstrated that monitoring the formation of hydroxyapatite in the first weeks of soaking allows for assessing short-term bone regeneration progress. In particular, SiO\({}_{2}\)-based materials can form hydroxyapatite as early as the day of implantation and continue to do so for up to 4 weeks, with a consistent increase over time [54], and after 21 days of soaking there is strong hydroxyapatite formation within a regular round shape. In our case, the spectra underlined for both the sample the presence of stretching and bending vibrations of phosphate groups (at 635, 560, and 470 cm\({}^{-1}\)) [55; 56], which were absent in the spectra of the as-synthesized samples (see Figures 4 and 5). Moreover, the SEM images revealed the presence of a typical  
Figure 7: FTIR spectra of SiO5, SiO5-HNO\({}_{3}\), and standard quercetin in the carbonyl stretching band range and hypotheses of structural modifications of the C ring of quercetin. Blue arrow indicates peak shift.  
globular shape of hydroxyapatite structure [57], whose Ca/P ratios (see Table 1) from EDS analysis were 1.67 for the SiQ5-HNO\({}_{3}\) system and 1.73 for the SiQ5 system, respectively. The obtained values are close to those reported in the literature [58].
# Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis
## 7 Conclusions  
In this work, we presented various attention-based models, including global-attention, self-attention and hierarchical-attention models. We implement and perform experiments on attention-based methods on RNNs, and in particular LSTMs and GRUs. The attention-based methods implemented in this work can be applied to any text classification task as well as other tasks and fields.  
Overall, the attention mechanism consistently improves the accuracy of baseline models. From the experimental results, it is observed that the proposed deep neural networks utilizing attention mechanisms achieve very high performance, thus proving that they are potent and suitable tools for textual sentiment analysis as well as classification tasks in general. The classification performance that was achieved showcases the prowess and scalability of the proposed approach in analyzing users' opinions and attitudes. Furthermore, the approach that achieved the highest accuracy across the board was an LSTM on top of pre-trained 300-dimensional word2 vec embeddings, which are further refined through additional training. The experimental study revealed various rather interesting findings. The best accuracy result was reported by the self-attention-based LSTM with the addition of word2 vec embeddings, consolidating the fact that the examined attention-based models lead to a performance gain of up to 3.5%.  
It is apparent that achieving performance gains in classifying sentiment and emotions is vital in natural language processing and particularly useful in a variety of systems and applications. Recognizing emotions, sentiments and attitudes in textual data can greatly assist in understanding peoples' opinions and is highly sought after by businesses and service providers. As a result, the attention mechanism has shown to be a strong performer, which can satisfy the aforementioned needs. A direction of future work could be to explore sequences of very high length and examine the operation of local attention or other attention mechanisms on such sequences. In addition, another direction for future work concerns the examination of self-attention, global-attention and hierarchical-attention mechanisms under alternative deep learning architectures and training parameters, such as different dropout rates. This constitutes the main direction that future work could focus on.  
Conceptualization, I.P. and S.K.; methodology, I.P. and S.K.; software, I.P. and S.K.; validation, I.P., S.K. and F.G.; formal analysis, S.K. and F.G.; writing--original draft preparation, I.P., S.K. and F.G.; writing--review and editing, I.P., S.K. and F.G.; supervision, I.P. and I.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in [48; 49; 50].  
The authors declare no conflict of interest.
## 7 Impact Assessment of EVs
### Impact of EV Integration on Grid
#### 7.3.1 Impact of EV Integration on Grid Stability  
Power system stability is defined as the ability of a power system to restore itself to its steady-state operational condition after experiencing a disturbance. Multiple instances of blackouts have been documented as a result of power system instability, thereby underscoring the importance of conducting research on system stability. EVs, when charging from the grid, exhibit non-linear load behavior that differs from conventional loads. This can potentially exert stress on the power system. The estimation of the behavior of this new load is further complicated by the unpredictable characteristics of EV charging locations, times, and durations. Concerns may arise regarding the stability of the power system in the event of a substantial influx of EV charging simultaneously. The accurate modelling of EV loads is essential for conducting stability research, as load characteristics can significantly impact power system stability. Several studies have been conducted to explore different EV load models [137; 138; 139]. Table 22 provides an elucidation of the impact of integrating EVs into the power grid considering different aspects of power system stability, voltage stability, frequency stability, and oscillatory stability.
# Review of Satellite Interferometry for Landslide Detection in Italy
###### Abstract  
Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.  
Keywords:landslide monitoring; landslide mapping; InSAR; Italy; multi-temporal SAR; risk management; InSAR applications +
Footnote †: journal: remote sensing
###### Abstract  
In order to address the issues of image quality degradation and distortion that arise in the context of video transmission coding and decoding, a method based on an enhanced version of CycleGAN is put forth. The lightweight attention module is integrated into the residual block of the generator module structure, thereby facilitating the extraction of image details and motion compensation. Furthermore, the perceptual function LPIPS loss is increased to align the image restoration effect more closely with human perception. Additionally, the network training method is modified, and the original image is divided into 128 \(\times\) 128 small blocks for training, thus enhancing the network's accuracy in restoring details. The experimental results demonstrate that the algorithm attains an average PSNR value of 30.1147 on the publicly accessible YUV sequence dataset, YUV Trace Dataset, which is a 9.02% enhancement compared to the original network. Additionally, the LPIPS value reaches 0.2639, representing a 10.42% reduction, and effectively addresses the issue of image quality deterioration during transmission.  
video coding; image reconstruction; GANs; PSNR; LPIPS
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
###### Abstract  
The emerging connected and autonomous vehicles (CAVs) challenge ad hoc wireless multi-hop communications by mobility, large-scale, new data acquisition and computing patterns. The Named Data Networking (NDN) is suitable for such vehicle ad hoc networks due to its information centric networking approach. However, flooding interest packets in ad-hoc NDN can lead to broadcast storm issue. Existing solutions will either increase the number of redundant interest packets or need a global knowledge about data producers. In this paper, a Location-Based Deferred Broadcast (LBDB) scheme is introduced to improve the efficiency and performance of interest broadcast in ad-hoc NDN. The scheme takes advantage of location information to set up timers when broadcasting an interest. The LBDB is implemented in V-NDN network architecture using ndnSIM simulator. Comparisons with several existing protocols are conducted in simulation. The results show that LBDB improves the overhead, the average number of hops and delay while maintaining an average satisfaction ratio when compared with several other broadcast schemes. The improvement can help offer timely data acquisition for quick responses in emergent CAV application situations.  
Keywords:Named Data Networking; Ad-Hoc Networking; Broadcast Storm Problem +
Footnote †: journal:
## 7 Results  
### Experimental results  
A quantitative evaluation of the performance of our CAD has been conducted for the detection of lesions above \(6\) mm and \(10\) mm, respectively. As mentioned previously, the testing data includes \(202\) patients (\(404\) volumes), with \(119\) lesions above \(6\) mm and \(75\) lesions above \(10\) mm. A CAD mark is considered successful if the point lies within the ground truth annotation. Free-Response Operator Characteristic (FROC) curves plotting the sensitivity vs. false positives appear in Figure 8, showing the detection performance at these two size thresholds. The points along the curve demonstrate the performance after successive stages in the cascade. Although the entire FROC curve is shown, the performance at the operating point available in the commercially available product (Medicsight ColonCAD 4.0) is indicated in the plot. At this point, the CAD system achieves an overall per-polyp sensitivity of \(95\%\) (for polyps \(10\)mm and greater) and \(90\%\) (for polyps \(6\) mm and greater), with the same false positives of 4.5 per volume on average. These results are summarized in Table 3.  
Figure 9: Examples of detected polyps and false positives, (a)-(l) are examples of detected polyps, (m)-(n) are examples of false positives. In particular, successfully detected sessile polyps appear in (e), (i), and (k), pedunculated polyps in (d) and (h), and polyps on folds in (a), (j), and (l). False positives shown in the figure relate to the ileococcal valve (m) and a thick fold (n).  
Figure 9 gives visual examples of detected polyps (a)-(l) and false positives (m)-(n). The CAD system accurately detects sessile polyps (e.g., (e), (i), (k)) as well as pedunculated polyps ((d), (h)) and polyps on haustral folds ((a), (j), (l)). One common source of false positives is the ileococcal valve (ICV), which is the valve that connects the colon to the small bowel. The ICV often appears as a protruding shape in the colonic lumen and is picked up by the CAD as a false positive, shown in (m). A false positive appearing on a thick haustral fold is shown in (n). Some true positive diminutive polyps (less than \(6\) mm) may be detected by the CAD, as shown in (k)-(l). These polyps cannot be avoided entirely since the match between the detected region diameter and the actual lesion diameter is not exact in all cases.