esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Termite Attack Tests  
The susceptibility of the insulation materials was evaluated for two subterranean termite species: _Reticulitermes graasei_ [PERSON] and _Reticulitermes flavipes_ [PERSON]. The former is one of the most common species in the south of Europe, and the only one that has been identified in mainland Portugal [28; 29]. _R. flavipes_ is an introduced, though well-established, species in France. The termites were collected in the fields from Portugal (_R. graasei_) and France (_R. flavipes_) and were maintained under optimal conditions until testing.  
The tests were performed based on an adaptation of the guidelines defined in standard EN 118 [30]. First, three cylindrical pieces of each insulation material (MW, EPS-S, EPS-C, and ECA) were cut and compacted into glass tubes. Then, the glass tubes were stacked on pine wood pieces using a contact glue, and a mixture of sand and water in a 4:1 proportion was introduced on the top of each tube. Two hundred and fifty workers, two soldiers, and two nymphs were introduced in each glass tube. The tubes were then sealed with a piece of cotton and an aluminum foil to avoid water evaporation (Figure 2). The controls were also performed without any insulation material.  
\begin{table}
\begin{tabular}{c c c} \hline
**Intensity of Growth** & **Evaluation** & **Covering of the Sample’s Surface** \\ \hline
0 & No growth apparent under & 0\% \\  & the microscope & \\
1 & Traces of growth & \textless{}10\% \\
2 & Light growth & 10–30\% \\
3 & Moderate growth & 30–60\% \\
4 & Heavy growth (to complete & 60–100\% \\  & surface coverage) & \\ \hline \end{tabular}
\end{table}
Table 1: Guidelines for the visual assessment of fungal growth adapted from ASTM D 5590-00 [26].  
The test specimens were kept inside a conditioned room at 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C and 80% \(\pm\) 5% RH for four weeks. After this period, the surviving termites were removed from the glass tube and counted, and the test specimens were disassembled and cleaned. The survival rate (SR) of each test specimen was determined (Equation (1)):  
\[\text{SR}\left(\%\right)=[\text{(nr. live workers/250)}\times 100]. \tag{1}\]  
Every test specimen was visually examined, and the attack degree was classified from 1 to 4, according to the standard EN 118 [30]: 0--no attack; 1--attempted attack; 2--slight attack; 3--average attack; and 4--strong attack.  
The analysis of variance (ANOVA) statistical test was performed to assess the susceptibility of the different insulation materials to termites, along with a post-hoc Tukey test, with a significance level of \(p<0.05\).
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
### Related Work  
Literature on exploiting medical image analysis and deep learning (DL) to classify or segment CT or CXR data of COVID-19 patients recently exploded (for reviews, see [PERSON] et al. [38], [PERSON] et al. [39]). More than 300 publications on this topic appeared only in 2020 [40]. For instance, [PERSON] et al. [2] achieved equal sensitivity (but lower specificity) compared to senior radiologists in detecting COVID-19 from CT and clinical information. A meta-analysis on COVID-19 revealed a mismatch between clinical and ML communities and found that US is significantly under-explored by ML researchers [40].  
With the rise of deep learning in computer vision, learning-based approaches to medical ultrasound analysis became increasingly popular over the last years [41]. While more than 50 papers were published on deep learning on US in 2017 [41], sparse work has been done on _lung_ US, with B-line detection/quantification being the most common task [42; 43; 44]. Others focus on pleural line extraction [45], subpleural pulmonary lesions [46] or lung cancer detection [47].  
In our initial preprint dubbed POCOVID-Net[48], we were first to regard the problem of automatic differential diagnosis of COVID-19 from LUS data. The released dataset has been incorporated by several authors into their work [49; 50; 51; 52]. Others developed a quality assessment module for COVID-19 pattern detection [53] or a classifier for typical LUS patterns of COVID-19 patients [54; 55]. Especially related to our work is the preprint by [PERSON] et al. [56] which proposed a model to differentiate types of B-lines according to the clinical diagnosis (COVID-19, non-COVID-19-ARDS, hydrostatic pulmonary edema). However, none of the above works have released their utilized datasets to the public.  
An exception to the neglect of US during the first months of COVID-19 a deep learning approach for a severity assessment of COVID-19 from US data [57]. The work convincingly predicts disease severity and segments COVID-19 specific patterns building up on their previous work on localizing B-lines [42]. The paper claims to release a segmentation dataset of COVID-19 cases, but to date, only a small fraction of class-labelled data and no segmentation annotations are available. While their effort (and follow-ups [45; 58; 59]) on severity assessment are highly relevant for disease monitoring, they are not applicable for diagnosis, where the main problem lies in _distinguishing_ COVID-19 from other lung pathologies (i.e., clinical diagnoses). We aim to close this gap with our approach to classify COVID-19, healthy, and bacterial pneumonia from LUS data.
## 3 Method
### Model  
The overall architecture of the proposed model named BIBC for NER is shown in Figure 2. As is shown by the figure, the whole model structure consists of four parts, namely BERT, IDCNN, bidirectional LSTM, and CRF. A modified BERT-WWM model is used to train on a massive unlabeled corpus for feature supplementation, which greatly enhances the semantic representation between words or phrases through deep and continuous learning. In addition, the IDCNN layer is added to make the model take into account both local and global features by IDCNN and bidirectional LSTM, thus making the whole model achieve better results on diabetes medical literature. The specific implementation process of the BIBC-based named entity recognition model consists of the following parts:  
1. Firstly, the sentences are input to the BERT-WWM pre-trained language model based on whole-word masking for pre-training to obtain word vectors that better match the Chinese expressions.
2. The obtained vectors are then sent into the IDCNN layer to obtain the local information of the sentences.
3. The vector sequences obtained from IDCNN layer become the input to BiLSTM to further encode the global sequence features of the sentences.
4. Finally, the entities are labeled using the label transfer function of CRF and the feature information extracted by each network to obtain a globally optimal label sequence.  
Figure 2: The model structure overview of BIBC.
# Dual-Gratings Imaging Spectrometer
## 3 Simulation of a Dual-Gratings Spectrometer  
Next, we design a dual-grating imaging spectrometer for H\(\alpha\), ionized calcium H-line and K-line hyperspectral imaging. Here, an optical design software CodeV is used to simulate the system. The focal length of the spectrometer was 600 mm, the number of pairs of double grating lines was 1350 L/mm, the numerical aperture of the object side was 0.05, and the size of the slit was 16 mm \(\times\) 5 \(\upmu\)m. The double gratings work at +1 and \(-\)1 order to avoid the influence of the high-order diffracted light of the gratings. Changing one reflector of the E-F system to two can better eliminate aberrations.This can correct coma in an asymmetrical state. This correction method comes from [PERSON]'s research [18]. In the simulation, in order to make the simulation closer to reality, data from two filters FB390-10 and FB650-10 from Thorlabs were used. The bandpass of the two filters are 380-400 nm and 640-660 nm, respectively. Therefore, it is only necessary to leave 396 nm-400 nm and 656 nm-660 nm imaging space between the two bands, and the dual bands will not overlap. The light path is shown in the Figure 12 and the system parameters are shown in Table 2.  
Because the double gratings work in the +1 order and -1 order, the angle between the double gratings is relatively large, as shown in the Figure 12.  
According to the data in Table 3, the MTF (modulation transfer function) of the full field-of-view system is higher than 0.6 at 32 L/mm, indicating that the system image quality is good. The spectral resolution is as high as 0.005 and 0.006 for each band, which is enough to meet the observation requirements of ultra-spectral resolution. The system can simultaneously output 1000 spectrum data near the H\(\alpha\) band and 1234 spectrum data near the H-line and K-line of ionized calcium.  
In the case of using the same grating, two imaging spectrometers with the same spectral resolution designed according to the traditional scheme can obtain the nearly same parameters as the dual-grating system, and the system volume can be 0.028 m\({}^{3}\) according to the simulation, which is almost twice that of the dual-grating system.
# A Taxonomy for Threat Actors' Delivery Techniques
## 2 Background
### Cyber Kill Chain  
The Cyber Kill Chain(r) framework [7], developed by Lockheed Martin, is part of the Intelligence-Driven Defense(r) model for the identification and prevention of cyber intrusion activity, identifying what a threat actor must complete in order to achieve its ite. It was first described in [7] as a seven-step process suitable for CNA or CNE operations, as shown in Figure 1.  
These seven steps are defined as follows [7,8]:  
1. Reconnaissance. Research, identification, and selection of targets.
2. Weaponization. Before attacking a target, the threat actor has to couple a remote access Trojan with an exploit into a deliverable payload.
3. Delivery. The transmission of weapons to the targeted environment to launch a particular operation.
4. Exploitation. After the weapon is delivered, the exploitation triggers an intruders' code.
5. Installation. The installation of an implant, just as a remote access Trojan, a backdoor, or any kind of malicious software, on the victim system, allowing the adversary to maintain persistence inside the environment.  
Figure 1: Cyber Kill Chain® as defined in [7].  
* Command and Control. The compromised hosts must beacon outbound to an Internet controller server to establish a C2 channel, thus allowing the threat actor to remotely control its target.
* Actions on objectives. After progressing through the first six phases, the intruders can take actions to achieve their original goals, such as information theft, denial, or hop to a third-party infrastructure.  
The cyber kill chain represents an industry-accepted methodology for understanding how an attacker will conduct the activities necessary to cause harm to an organization and has been largely discussed [9] (in ref. [10], the authors identify some of the discussions regarding the application of the Cyber Kill Chain). Some authors [11; 12] have proposed the addition and removal of different stages in order to improve or adjust the original model, and the topic has also been discussed in technical conferences. Moreover, some efforts to unify models and variants, such as [13], have been made. Despite this, the original proposal has been widely used and applied to specific problems regarding advanced threat actors, such as those related to the modeling of the attack stages against critical infrastructures [14; 15; 16; 17].
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Discussion and Limitations  
Feature distillation models have improved gradually, starting with the IDN, which simply utilized basic information distillation. This transitioned to the IMDN, which included various distillation processes. Building on the IMDN, residual learning was included, resulting in the RFDN. Subsequent feature distillation models improved the SRB established in RFDN. These KD models are based on the idea of taking existing SR models and decreasing their weight and computational needs utilizing the TS structure. Furthermore, the proper initialization of student models is critical for training these models. Furthermore, ref. [66] used novel feature similarity ranking to increase task performance. While distillation-based approaches that use channel splitting for feature distillation have shown good results, they are limited by the convolution kernel, which restricts their ability to capture long-range relationships. To solve this issue, attention-based approaches have been developed, which enable the identification of long-range relationships using blocks capable of extracting non-local information.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 4 Methods
### Cross-Domain Knowledge Learning Module  
In different graph domains, feature information exhibits significant heterogeneity, whereas many fundamental structural properties remain common across domains, making them suitable for shared learning potential. Therefore, to capitalize on this property, this paper explores a server-side mechanism whereby cross-domain public data are shared through a server to provide a global view and help models better understand and learn generic structural features. Although public-domain graph data differ substantially from network traffic graphs in semantics and application scenarios, they exhibit rich and widely observed structural characteristics, such as community structures, degree distribution heterogeneity, and path length patterns. In this study, we do not treat these public graphs as direct substitutes but rather as sources of structural priors to guide the learning of anomaly detection models.  
Specifically, the server holds data from other public domains (e.g., data from social networks, small molecule data in chemistry or protein structure data in biology) and utilizes these data to train a global model. According to Equation (10), the server utilizes the public domain dataset \(D_{\text{pub}}\) to train a global model \(\theta_{\text{pub}}\), where \(L_{\text{pub}}\) denotes the loss function computed over the public data:  
\[\theta_{\text{pub}}^{l+1}=\theta_{\text{pub}}^{l}-\eta\
abla L_{\text{pub}}( \theta_{\text{pub}}^{l};D_{\text{pub}}) \tag{11}\]  
During the aggregation phase, as shown in Equation (11), the server aggregates the structure model parameters uploaded by all clients and combines them with the model parameters \(\theta_{\text{pub}}\) trained on the public data to generate a new global model \(\theta_{\text{global}}\):  
\[\theta_{\text{global}}^{t+1}=\lambda\theta_{\text{pub}}^{t+1}+(1-\lambda) \sum_{k=1}^{K}\frac{n_{k}}{N}\theta_{k}^{t+1} \tag{12}\]  
where \(\lambda\in[0,1]\) is a weighting coefficient that adjusts the contribution of the public data to the global model. \(K\) denotes the number of clients, \(n_{k}\) is the number of data samples held by the client \(k\), and \(N=\sum_{k=1}^{K}n_{k}\) is the total number of samples across all clients. During model aggregation, the server utilizes this cross-domain public data to enhance the learning of generalizable structural features, thereby refining the global model that guides local clients. In this case, the server not only performs the conventional role of aggregating model parameters but also acts as a bridge for cross-domain data transfer, enabling knowledge sharing across different domains. This approach enhances the adaptability of the federated learning framework in complex and diverse data environments.
###### Abstract  
Improving the phase sensitivity of the optical fiber Sagnac sensor is very important for accurately detecting weak signals of acoustic emission. Theoretical analysis shows that the initial phase of the sensor is \(\pi\) under ideal conditions, and the maximum phase sensitivity is obtained when the bias phase is \(\pi/2\). In this work, an experimental system was built with an aluminum alloy plate as the experimental object. The initial phase of the sensor was modulated by a Y-branch waveguide, and the fitting curve of the experimental data was in good agreement with the curve of the numerical simulation. Moreover, our experiments show there was a single value for the bias phase of \(\pi/2\), which significantly deviated from the theoretical value. The results show that the greatest phase sensitivity of the sensor not only could be increased by nearly nine times through modulating the initial phase, but also could suppress the harmonic interferences in the sensing system. This study can provide a useful reference for improving the phase sensitivity of the optical fiber Sagnac AE sensor in practical applications.  
Keywords:optical fiber Sagnac sensor; phase sensitivity; acoustic emission; phase modulation; harmonic interference +
Footnote †: journal: Applied Sciences
## 2 Precision Medicine and Cardiology
### Myocardial Infarction
#### 2.1.1 Metabolomics  
The association between an individual's metabolic profile and MI has been explored a lot recently. [PERSON] et al. demonstrated an independent association between peripheral blood metabolites and the presence of CAD (coronary artery disease). They showed that simple metabolites such as factor 4 (branched-chain amino acid metabolites) and factor 9 (urea cycle metabolites) can help diagnose CAD [48; 50]. Many new metabolites have recently been associated with CAD, including but not limited to: medium-chain acylcarnitines, short-chain dicarboxylacrylcarnitines, long-chain dicarboxylacrylcarnitines, long-chain acylcarnitines, short-chain acylcarnitines, medium-chain acylcarnitines, ketone related, cholesterol, lipids, fatty acids, glucose, and branched-chain amino acids [49]. Another recent observational study by [PERSON] et al. found that low serum albumin levels are associated with adverse events in STEMI patients [46]. [PERSON] et al. and [PERSON] et al. showed that supplementing patients with BCAAs (branch-chain amino acids) showed increased serum albumin levels [51; 52]. The combination of one or more newer metabolic markers can aid the diagnosis for a certain subset of patients, personalizing their care and increasing the sensitivity of MI detection.  
Metabolomics can improve treatment options in addition to its predictive and diagnostic capabilities. Currently, the mainstay of acute coronary syndrome treatment is revascularization via emergency percutaneous coronary intervention (PCI). [PERSON] et al. proposed ways to improve PCI by using early and prolonged glutathione infusions to blunt the inflammatory response via a chain of processes: 1. reduced NOX2 activation; 2. hsCRP generation; 3. TNF- levels; 4. cTpT release; 5. reduced neutrophil generation to protect myocardial cells; and 6. prevention of aberrant cardiac remodeling, allowing better left ventricular size and function post-PCI [46].
## 3 Results  
In this section, the results of both scenarios are presented, followed by an analysis of the findings to evaluate their implications and performance. The results obtained from the optimized hybrid scenarios demonstrate a significant impact on rural energy planning. Scenario I provides a cost-effective transition toward renewables, reducing operational costs without compromising reliability. Scenario II substantially enhances energy autonomy and resilience, significantly lowering dependency on grid power and reducing emissions. These outcomes indicate that integrating locally sourced biomass alongside PV offers considerable socio-economic and environmental benefits, improving energy security, stability, and local economic resilience. Thus, these scenarios provide actionable insights for policymakers and stakeholders seeking sustainable and reliable energy solutions for rural communities.  
Figure 7: Design process in HOMER.  
Before exploring the outcomes in greater depth, it is essential to understand how HOMER decides when each component (e.g., a generator or battery) should produce or store electricity. In HOMER, power dispatch refers to how the software decides when each component (such as a generator or battery) should produce or store electricity. Common strategies include Cycle Charging (CC) and Load Following (LF). In CC, the generator runs near its rated capacity whenever it is on, providing enough power for the load and charging the battery with surplus energy. This approach often leverages higher generator efficiency by operating it near optimum load points. Conversely, in LF, the generator meets immediate demand without intentionally overproducing to charge the battery. This can reduce unnecessary fuel consumption but may run the generator at less efficient partial loads if the system's demand fluctuates. By comparing these dispatch strategies in HOMER, designers can evaluate trade-offs in fuel usage, operational costs, and overall system performance.
# A Stock Prediction Method Based on Heterogeneous Bidirectional LSTM
## 4 Experiment
### Comparison of Model Convergence Speed  
In terms of model running speed, the time for one iteration of both He-BiLSTM and BiLSTM is recorded, with the first 10 iterations being counted. The statistical results of the running time are shown in Table 4.  
Figure 8: Comparison of He-BiLSTM and BiLSTM in gold futures price forecasting.  
Figure 6: Comparison between He-BiLSTM and BiLSTM in Microsoft stock price prediction.  
Figure 7: Comparison between He-BiLSTM and BiLSTM in Kweichow Moutai stock price prediction.  
From Table 4, it can be observed that as the number of iterations increases, the advantage of He-BiLSTM in terms of reduced time consumption becomes increasingly evident. After 10 iterations, He-BiLSTM takes a total of 159.378 s, while BiLSTM takes 172.683 s. Compared to BiLSTM, He-BiLSTM's time consumption is reduced by 7.70%.
###### Abstract  
Since opportunities for spaceflight experiments are scarce, ground-based microgravity simulation devices (MSDs) offer accessible and economical alternatives for gravitational biology studies. Among the MSDs, the random positioning machine (RPM) provides simulated microgravity conditions on the ground by randomizing rotating biological samples in two axes to distribute the Earth's gravity vector in all directions over time. Real-time microscopy and image acquisition during microgravity simulation are of particular interest to enable the study of how basic cell functions, such as division, migration, and proliferation, progress under altered gravity conditions. However, these capabilities have been difficult to implement due to the constantly moving frames of the RPM as well as mechanical noise. Therefore, we developed an image acquisition module that can be mounted on an RPM to capture live images over time while the specimen is in the simulated microgravity (SMG) environment. This module integrates a digital microscope with a magnification range of \(20\times\) to \(700\times\), a high-speed data transmission adaptor for the wireless streaming of time-lapse images, and a backlight illuminator to view the sample under brightfield and darkfield modes. With this module, we successfully demonstrated the real-time imaging of human cells cultured on an RPM in brightfield, lasting up to 80 h, and also visualized them in green fluorescent channel. This module was successful in monitoring cell morphology and in quantifying the rate of cell division, cell migration, and wound healing in SMG. It can be easily modified to study the response of other biological specimens to SMG.  
microgravity simulation; cell culture; live cell imaging; random positioning machine +
Footnote †: journal: Applied Physics Letters