esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 3 Materials
### ICD-10 Dataset  
The **CodiEsp** corpus [39] consists of 1000 clinical cases, tagged with various ICD-10 codes by health specialists. This dataset is released in the context of the CodiEsp track for CLEF ehealth 2020. The dataset corresponding to the subtask of classifying diagnostic ICD codes is used. The original text fragments are in Spanish but an automatically translated version in English is also provided by the organizers, this version is used in this research. The publicly available dataset contains a split of 500 training samples, 250 development samples, and 250 test samples. In total, the 1000 documents comprises of 16,504 sentences and 396,988 words, with an average of 396.2 words per clinical case. The biggest hurdle while training with this dataset is the size and consequently the small number of training examples for each category present. Figure 4 gives a sorted view of all categories present in the training dataset and the amount of examples tagged with that specific category.  
There are in total 1767 different categories spread out over only 500 training documents. Every document is labeled with on average 11.3 different categories and each category is on average represented by 3.2 training examples. Even the top 50 most frequently occurring categories have only between 15 and 112 corresponding positive training documents. Therefore, tests for this dataset are conducted on these 50 categories. Table 1 gives an overview of statistics for all discussed training datasets. The specifics for the corresponding development and test sets are similar. Displayed statistics for the Dis and the Full dataset are the same since the only difference lies in larger text fragments, resulting in 72,891 unique tokens for the Full dataset compared to 51,917 for Dis. There are no differences concerning the labels.  
Figure 4: Category frequencies of CodiEsp training dataset.
## 3 Proposed Mechanism
### Network Load Detection (NLD)
If the \(m^{\prime}\)-th lost packet is recovered by end-to-end loss recovery where \(1\leq m^{\prime}\leq n\), the loss intervals of the last \(n\) losses are changed in the following way:  
\[d_{i,m}=\left\{ \begin{array}{ll}d_{i,m},&m<m^{\prime}\\ d_{i,m}+d_{i,m+1},&m=m^{\prime}\\ d_{i,m+1},&m^{\prime}<m\leq n+1.\end{array} \right. \tag{2}\]  
If the loss rate of any flow exceeds the threshold, then we say that the channel used by the flow is overloaded. The NLD also measures the active load of the network (_i.e._, the number of packets sent per unit time). The sink uses a timer for this. When the timer expires, it finds the sequence number of the most recently received packet of each flow and restarts the timer. If the two most recently recorded sequence numbers of the \(i\)-th flow are \(s_{1}\) and \(s_{2}\), then the number of packets sent for the flow is \(S_{i}=s_{2}-s_{1}\). The current load of a tree-branch, \(curr\_load[tb]\), consisting of \(F\) flows is: \(\sum_{i=1}^{F}S_{i}\). The average instantaneous load, \(avg\_load[tb]\), of a TB is measured by using the exponentially weighted moving average (EWMA) method as shown:  
\[avg\_load[tb]=\alpha\times curr\_load[tb]+(1-\alpha)\times avg\_load[tb], \tag{3}\]  
where \(\alpha\) is a tuning parameter that is used to smooth the value of the average load of a TB. Through extensive simulation, we have set the value of \(\alpha\) to 0.12 which produces the best estimation for a long-term average TB load.
## 2 Description of the System under Analysis  
Figure 1 shows the principle diagram of the system under analysis, which is divided into two parts: the power part, i.e., the AC system which is used to deliver power, and communication part, i.e., the system which enables data communication through the power line. The power part of the system represents a grid connected PV based renewable energy system and composed of Photovoltaics (PV)/battery, boost converter, an inverter, three-phase transmission line, and AC grid. The boost converter boosts the battery/PV voltage to the desired DC link voltage of the inverter. The output of the inverter is connected to the AC grid through a three-phase transmission line. The three-phase load is fed from the PV and the grid simultaneously [17].  
The measured load voltage (in this case, the communication signal) is transmitted to the inverter side by using PLC. To this end, the communication signal is modulated by QPSK modulation and coupled to the AC system using RC couplers and demodulated by the QPSK demodulator connected at the inverter output. In addition to this, RPWM is implemented and applied to the DC-DC converter, whereas conventional PWM is applied to the inverter in order to analyze only the effects of Random Modulated DC-DC converter on the PLC system.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Atmospheric Influences
#### 4.1.2 Low-Altitude Sensing  
Although UAVs typically fly at lower altitudes (30-500 m) than aircraft, atmospheric effects are still strong for this range of target-sensor distances and must be compensated for prior to estimating fluorescence using passive remote sensing techniques. Similarly to other airborne acquisitions, atmospheric effects on UAV-acquired data mainly depend on (1) the platform altitude and (2) the concurrent atmospheric conditions. However, even in those cases in which the UAV is flying at lower altitudes, e.g., 100 m, atmosphere effects affect fluorescence measurements. Figure 7 shows the difference between simulated acquired upwelling radiances at the sensor level (UAV at 100 m) and at the surface level for two Aerosol Optical Thickness (AOT) settings, i.e., 0.03 [-] and 0.15 [-].  
Aerosols significantly affect shorter wavelengths; i.e., they have a stronger impact on the O\({}_{2}\)-B than the O\({}_{2}\)-A spectral region. This can be observed in Figure 7 where the values outside the oxygen absorption regions, especially in the left panel, differ from 0 mW/m\({}^{2}\)/sr/nm. As expected, when the aerosol content is higher (red solid line), this effect is more noticeable. On the contrary, inside the oxygen absorption band, the spectral behavior is mainly dominated by the upward surface to the sensor oxygen transmittance.  
Hence, although atmospheric effects can be neglected for many applications at the UAV scale, this is not the case when measuring the subtle fluorescence signal using passive remote sensing techniques and exploiting oxygen absorption features. In this respect, the atmospheric  
Figure 7: Difference in radiance units between radiance simulated at the UAV level (100 m) and at the TOC level (0 m) for the O\({}_{2}\)-B region (left) and the O\({}_{2}\)-A region (right). Simulations were performed with the MODTRAN 5.02 RTM using the mid-latitude summer atmospheric model and the default rural aerosol model at a spectral resolution of 1 cm-1, i.e., around 0.05 nm at the O\({}_{2}\)-B and 0.06 nm at the O\({}_{2}\)-A.  
compensation techniques typically applied at the airborne scale can be equally suitable for correcting UAV-acquired measurements.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
###### Abstract  
Wireless sensor networks (WSNs) grapple with a challenging and pivotal issue: how to maximize the network's lifespan. To improve the quality of service (QoS) and extend the life of the network, there has been a lot of effort made in this area in recent years. The sensor nodes of a WSN are autonomous, dispersed devices that gather and direct data to a central hub, or \"Base Station\", using wireless connections without any central coordinator. These networks have less processing power, memory capacity, power supply, and so on, which limits their range and battery life. Numerous studies preceding our research have proposed a myriad of strategies to enhance network longevity. In a WSN, information is relayed from one node to the next until it reaches the base station. Most nodes may be reliably expected to operate for the duration of their batteries. These strategies encompass reducing energy consumption, minimizing latency, load balancing, clustering, efficient data aggregation, and curtailing data transmission delays. WSNs may alter dynamically as a result of internal or external circumstances, necessitating a depreciating dispensable redesign of the network. Because the networks in classic WSN techniques are expressly programmed, it is difficult for them to respond dynamically. Machine learning (ML) approaches can be used to respond appropriately in order to overcome such circumstances. Machine learning is the process of acting without human involvement or reprogramming in order to learn from experiences. This paper presents a review of different ML-based algorithms for WSNs together with their benefits, limitations, and parameters that affect network longevity.  
machine learning; wireless sensor network; routing; neural network; fuzzy logic; reinforcement learning 2023  
play a role. Due to the significance of node placement in WSNs, a few research publications have developed mathematical formulations for the best possible node distribution schemes. While some studies focused on using dispatch, a node's sole purpose is to relay packets rather than do recognizing, and others emphasized the need to strategically place relay nodes at various distances from the master station. An energy-effective WSN design considers factors such as cluster-head selection, cluster size, and the frequencies with which cluster member characteristics are updated. Although some employ on/off switches, others still aggregate data to find patterns that can lead to energy savings. It is possible that the time and energy put into finding a solution to this problem can be reduced by using the techniques described in [1]. Similarly, \"hierarchical or cluster-based routing\" would be a particularly useful methodology for more scale WSNs to address the challenges of scalability, effective communication, and fault acceptance. Clusters are subnetworks organized in a hierarchical structure.
## 3 Compressive Behavior Evaluation of the Hybrid\(-\)Cfft
### Compressive Strength Testing of the Hybrid-CFFT
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Concecele Design**} & \multicolumn{1}{c}{**FFR Thickness**} & \multicolumn{1}{c}{**Specimen No.**} & \multicolumn{1}{c}{**Feature Load (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ \mathbf{ }}}}}}}}}}}}\))**} & \multicolumn{1}{c}{**Facture Strength (MPa)**} & \multicolumn{1}{c}{**Displacement (mm)**} \\ \cline{3-8} \multicolumn{1}{c}{**Strength (\(f_{\text{\tiny{AF}}}\), MPa)**} & \multicolumn{1}{c}{**(\(t_{p}\) mm)**} & \multicolumn{1}{c}{**Experiment**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Experiment**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Experiment**} & \multicolumn{1}{c}{**Average**} \\ \hline \hline \multirow{8}{*}{21} & \multirow{8}{*}{2.8} & 1 & 3490 & \multirow{8}{*}{3252 \(\pm\) 245} & 49.37 & \multirow{8}{*}{46.00 \(\pm\) 3.47} & \multirow{8}{*}{11.04 \(\pm\) 1.62} \\  & & 2 & 2905 & & 41.10 & & 13.35 \\  & & 3 & 3140 & 3252 \(\pm\) 245 & 44.42 & & 12.39 \\  & & 4 & 3153 & & 44.61 & & 10.84 \\  & & 5 & 3570 & & 50.51 & & 9.28 \\ \cline{2-8}  & & 1 & 3845 & & 54.40 & & 16.45 \\  & & 2 & 4485 & & 63.45 & & 12.19 \\  & & 3 & 4330 & 4112 \(\pm\) 252 & 61.26 & & 13.79 \\  & & 4 & 3890 & & 55.03 & & 8.44 \\  & & 5 & 4010 & & 56.73 & & 11.99 \\ \cline{2-8}  & & 1 & 4220 & & 59.20 & & 8.31 \\  & & 2 & 4330 & & 61.26 & & 12.99 \\  & & 5.6 & 3 & 4365 & 4202 \(\pm\) 224 & 61.75 & & 12.44 \\  & & 4 & 3765 & & 53.26 & & 9.09 \\  & & 5 & 4330 & & 61.26 & & 12.36 \\ \hline \hline \multirow{8}{*}{30} & \multirow{8}{*}{2.8} & 1 & 3615 & \multirow{8}{*}{3656 \(\pm\) 80} & 51.14 & \multirow{8}{*}{51.72 \(\pm\) 1.14} & \multirow{8}{*}{9.15 \(\pm\) 2.19} \\  & & 2 & 3615 & & 51.14 & & 9.44 \\ \cline{1-1}  & & 3 & 3805 & 3656 \(\pm\) 80 & 53.83 & & 11.04 \\ \cline{1-1}  & & 4 & 3575 & & 50.58 & & 9.18 \\ \cline{1-1}  & & 5 & 3670 & & 51.92 & & 6.17 \\ \cline{1-1} \cline{2-8}  & & 1 & 4925 & & 69.67 & & 11.16 \\ \cline{1-1}  & & 2 & 4880 & & 69.04 & & 11.16 \\ \cline{1-1}  & & 3 & 4880 & 4899 \(\pm\) 45 & 69.04 & & 9.16 \\ \cline{1-1}  & & 4 & 4707 & & 70.31 & & 9.28 \\ \cline{1-1}  & & 5 & 4840 & & 68.47 & & 15.52 \\ \cline{1-1} \cline{2-8}  & & 1 & 4885 & & 69.11 & & 13.09 \\ \cline{1-1}  & & 2 & 4740 & & 67.06 & & 11.04 \\ \cline{1-1}  & & 3 & 5015 & 4743 \(\pm\) 238 & 70.95 & & 10.37 \\ \cline{1-1}  & & 4 & 4765 & & 67.41 & & 11.56 \\ \cline{1-1}  & & 5 & 4310 & & 69.97 & & 23.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of compressive strength testing of the hybrid-CFT.
## 5 PSI by Means of TSC: Results and Discussion
### Interferometric Processing Chain Considerations and CPT Overview
#### 5.1.2 Spotlight SAR Interferometry  
The azimuth resolution of a SAR image is mainly determined by the characteristics of the azimuth radiation pattern of the antenna. In the conventional strip-map mode, the resolution is roughly half the azimuth length of the antenna and the processing parameters, Doppler centroid and Doppler rate, are azimuth invariant. In this context, the antenna length cannot be arbitrarily reduced without the risk of causing azimuth and/or range ambiguities.  
In the sliding spotlight mode, the antenna pointing is constantly steered to maintain the radar footprint illuminating a fixed area on the ground during a time period longer than the conventional SAR aperture interval, thus achieving an azimuth resolution improvement. The steering of the antenna causes the spotlight raw data, as well as the focused image to present a systematic Doppler centroid drift in the azimuth direction. This drift rate \(f_{DR}\) of the image spectrum may be calculated from the Doppler values and their zero-Doppler time differences through the first and last azimuth time of the focused scene as follows [30]:  
\[f_{DR}=\frac{f_{DC,n}-f_{DC,1}}{t_{DC,n}-t_{DC,1}} \tag{2}\]  
where \(f_{DC,1}\) and \(f_{DC,n}\) account for the first and the last Doppler annotated values in the product and \(t_{DC,1}\) and \(t_{DC,1}\), their correspondent zero-Doppler corrected times.  
This characteristic must be taken into account in the following interferometric processing chain steps: co-registration, resampling and, when applied, common band filtering. Prior to applying these steps, the center frequency of the interpolation kernels in azimuth must consider the Doppler drift or images must be adequately base-banded.  
Each azimuth line must hence be demodulated with the following chirp function parameterized with drift rate [30]:  
\[c(t)=e^{-j\pi(t-t_{start})^{2}f_{DR}} \tag{3}\]  
where \(t_{start}\) refers to the azimuth start time of the scene.  
Once the drift rate is removed, the spectrum is centered on the start Doppler value. Then, the base-banding is performed, as is in the traditional strip-map case, multiplying the image in the spatial domain by a phase ramp in the azimuth parameterized with the zero-Doppler start value. The co-registration, resampling and common band filtering can be now applied. After these steps, both scenes are modulated back to their original frequency bands by multiplying them with the conjugate chirp \(c^{*}(t)\), and classical strip-map PSI techniques may be employed.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Dataset
#### 2.1.3 Data Structure for Learning  
Graphs and associated paths are preprocessed for PyTorch [86] and provided as.pt files. This preprocessing step is responsible for **(1)** converting the raw graph into an adequate tensor-based format and **(2)** ensuring that all graphs have the correct dimensions. The preprocessing script can be found in the replication package ([[https://github.com/mbdlrocks/PhD_Replication_Package/tree/master/Physics-Informed-GNN%20](https://github.com/mbdlrocks/PhD_Replication_Package/tree/master/Physics-Informed-GNN%20)]([https://github.com/mbdlrocks/PhD_Replication_Package/tree/master/Physics-Informed-GNN%20](https://github.com/mbdlrocks/PhD_Replication_Package/tree/master/Physics-Informed-GNN%20))(PIGNN)_Preprocessing_, accessed on 7 April 2025). In the dataset, denoted by \(\mathcal{D}=\{X_{i}\,Y_{i}\}_{i=0}^{n}\), each sample \(X_{i}=(A_{i},F_{i})\) is a tuple comprising an adjacency tensor \(A_{i}\in\mathbb{R}^{|V|\times|V|\times d}\) and a graph signal tensor \(F_{i}\in\mathbb{R}^{|V|\times p}\), with \(d\) the number of different edge types and \(p\) the node features. For a given graph \(X_{i}\), the label \(Y_{i}\in\mathbb{R}^{|V|\times|V|}\) is an adjacency matrix where \(Y_{ij}=1\) if an edge between nodes \(i\) and \(j\) is part of the attack path. The dataset comprises \(n=1033\) unique samples, each containing \(p=20\) node features and \(d=16\) unique edge types. The tensor-based format conversion is also illustrated in Figure 3.  
This dataset is the first of its kind and should be considered as a prototype for future enhancements. We purposefully restricted some of its aspects (e.g., the assumption that attacks originate from user accounts rather than public-facing systems or restriction to identity snowball attacks). These design choices, along with the reliance on automatically generated synthetic data instead of ground-truth samples, should be understood in the context of this paper's objective--demonstrating the efficiency of learning-based algorithms for APA. At the same time, they enabled the rapid development of a sufficiently robust  
Figure 2: (_top_) Distribution of the four edge types most associated with attack paths; (_bottom left_) correlation matrix of edge types; (_bottom right_) distribution of node degree in attack paths compared to overall node degree.  
experimental setting for testing our methods. For future enhancements or for organizations appropriating the dataset, start-node probabilities could be aligned with internal phishing campaign results. Another key aspect of this dataset is its extensibility through graph-based structures. Notably, existing cybersecurity knowledge graphs (CSKGs) [87; 88; 89; 90; 91; 92] can be used to add new nodes and (non-traversable) edges to environments [54]. This data augmentation proposal is discussed in Section 4.
# Assessing the Risk of Spreading COVID-19 in the Room Utilizing Low-Cost Monitoring System
## 2 Components
### Concept of System  
In Figure 1, we can see the connection between the components. All sensors, except for the smart ring, are directly connected to the main control unit, which is the ESP32 development board. The control unit collects all data from the sensors, processes them, and then sends the data to the cloud using the Wi-Fi wireless communication standard. Based on the algorithm, possible risks that could lead to the spread of the respiratory disease COVID-19 are processed and evaluated. At the same time, the data from the cloud are displayed on the website. So, it is possible to configure the system to display the rooms in which the system is located. One or more systems can be placed in one room, which ensures higher reliability and accuracy of the system.
## 2 Related Works
These sensors capture changes in the pixel's intensity asynchronously, enabling ultra-low-latency capturing, heightened temporal resolution, and remarkable stability during motion as well as in high-dynamic-range environments [29]. MS-EVS--a Multispectral Event-based Framework for Face Detection [14]--demonstrates the benefits of the early fusion of spectral cues for face detection within dynamic scenes. Yet, the work was constrained by detection, ignoring model uncertainty and identity-level recognition, which are both fundamental for robust decision-making in complex environments. Uncertainty modeling has become a focal point in the context of deep learning due to the need to address unpredictability for real-world deployments [19]. Conventional neural networks are often overconfident in their predictions, and when wrong, confidence exacerbates the problem. Limited applicability to forensics, healthcare, and emergency response creates high-stakes reliance on trust in technology [30]. Bayesian neural networks (BNNs) model epistemic uncertainty in a principled fashion by integrating learning distributions over the weights of the model rather than employing a deterministic approach to values [31]. Exact inference in deep models is intractable, so approximate methods like Monte Carlo Dropout [32], Bayes by Backprop [15], and Deep Ensembles [33] have been formulated. These techniques are very effective in generating accurate predictions and are especially useful in detecting out-of-distribution samples or ambiguous inputs [34]. Motivated by these developments, we propose PMB-VM, which extends uncertainty modeling to face recognition by performing stochastic sampling across multiple lightweight branches informed by Bayesian regularization. This allows the model to produce high-confidence embeddings and accurately calibrated variance estimates without significantly increasing the embedding computational cost. Unlike prior models that decouple uncertainty estimation as an additional effort from significantly complex ensemble architectures, PMB-VM is seamlessly integrated into HUE-Net's pipeline and provides efficient performance. While earlier attempts have individually solved the problems of recognition accuracy, spectral variability, and uncertainty quantification, none have integrated all these facets into a single streamlined deployable framework. HUE-Net bridges this gap by combining multispectral event fusion, variational uncertainty modeling, and adaptive attention within an edge-efficient architecture. This enables the model to not just attain state-of-the-art recognition performance, but also reason about the quality of features and confidence in predictions--vital for real-world face recognition in ethically sensitive, mission-critical settings.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Threat Model  
Adversaries can compromise sensor nodes and exploit them to inject false reports and false votes in the network. Adversaries can also injection false reports through an outside entity. The frequency of the attacks increases gradually with an increasing number of nodes being compromised by the adversaries. Adversaries can insert a large number of fabricated reports and attach false votes to the legitimate reports to intensify the effect of the attacks. Compromised nodes belonging to the same cluster can communicate with each other to generate fabricated reports, whereas compromised nodes belonging to separate clusters cannot conspire with one another.