esa-sceva/satcom-chunk-collection huggingface dataset:
# ColorMedGAN: A Semantic Colorization Framework for Medical Images
## 2 Methodology
### Loss Function
#### 2.2.1 ColorCycleGAN Loss Function  
Consistent with the definition of CycleGAN, we define the colorization task as two sets of mapping directions: \(G:X\to Y\) and \(F:Y\to X\), where \(X\), \(Y\) represent the grayscale and color domains. \(x_{i}^{N}=1\) where \(x_{i}\in X\) and \(y_{j}^{M}=1\) where \(y_{j}\in Y\). The segmentation feature is defined as \(S_{x}=S(X)\), \(S_{y}=S(Y)\). The data domain distribution is denoted as \(x\sim\text{Pdata}(x)\), \(y\sim\text{Pdata}(y)\). In addition, there are two discriminators, \(D_{x}\) and \(D_{y}\), in the system, where \(D_{x}\) is used to distinguish the true \(X\) from the false \(X\) generated by \(F\) and \(D_{y}\) is used to distinguish the true \(Y\) from the false \(Y\) generated by \(G\). Therefore, the loss function in this part can be defined as Equation (2).  
\[\begin{split}\mathcal{L}_{\text{cycleGAN}}(G,F,D_{x},D_{y},S)& =\mathcal{L}_{\text{GAN}}(G,S,D_{y},X,Y)\\ &+\mathcal{L}_{\text{GAN}}(F,S,D_{x},Y,X)\end{split} \tag{2}\]  
where the first part denotes the loss functions of the forward synthesis, and the second part denotes the loss functions of the backward synthesis.  
For the image \(x\) in the \(X\) domain, the image generated after passing into \(G\) and then put into \(F\) to generate the \(X\) domain image back should be consistent with the original image, \(x\to G(x)\to F(G(x))\), \(y\) in the same way. So we can define the consistency loss function as Equation (3).  
\[\begin{split}\mathcal{L}_{cyc}(G,F)&=\mathbb{E}_{x \sim\text{Pdata}(x)}[||F(G(x))-x||_{1}]\\ &+\mathbb{E}_{y\sim\text{Pdata}(y)}[||G(F(y))-y||_{1}]\end{split} \tag{3}\]  
The consistency loss function is defined concerning the colorCycleGAN approach, using perceptual losses as computational objects instead of pixel points, thus enabling the style between \(X\) and \(Y\) to map, shown as Equation (4).  
\[\begin{split}\mathcal{L}_{ide}(G,F)&=\mathbb{E}_{y \sim\text{Pdata}(y)}[||VGG_{l}(G(y))-VGG_{l}(y)||_{1}]\\ &+\mathbb{E}_{x\sim\text{Pdata}(x)}[||VGG_{l}(F(x))-VGG_{l}(x)|| _{1}]\end{split} \tag{4}\]  
The gray loss function, for any RGB image, we can get the corresponding grayscale images through the following Equation (5), also introduced by colorCycleGAN, keeps the image structure invariant and slightly adjusts the generated image by calculating the loss of gray intensity using Equation (5). We got Equation (6) finally. The first half indicates that the image \(G(x)\) generated by grayscale MR is consistent with the gray intensity of the original image, and the second half indicates that the image \(F(y)\) generated by the anatomical image is consistent with the gray intensity of the input image.  
\[Gray(\text{img})=0.299r+0.576g+0.114b \tag{5}\]  
\[\begin{split}\mathcal{L}_{col}(G,F)&=\mathbb{E}_{x \sim Pdata(x)}[||Gray(G(x))-x||_{1}]\\ &+\mathbb{E}_{y\sim Pdata(y)}[||F(y)-Gray(y)||_{1}]\end{split} \tag{6}\]
## 5 DSM Using DGs and ESS
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DERs**} & \multicolumn{1}{c}{**Available for**} & \multirow{2}{*}{**Possible DSM Techniques**} & \multirow{2}{*}{**Types of Operations**} & \multirow{2}{*}{**Benefits**} \\  & **the Time** & & & \\  & **(24 h)** & & & \\ \hline \multirow{4}{*}{Solar [30; 31]} & Morning to & Peak clipping & Thermal: Converting solar heat energy to electrical energy. & Cleaner energy, reduction in the use of carbon. \\  & afternoon & Load switching & Photovoltaic: Converting solar radiations to electrical energy with solar cells. & Cleaner energy, tariff reduction, decentralized generation, residential mode generation. \\ \hline \multirow{2}{*}{Wind Energy [32]} & \multirow{2}{*}{24 h} & Load reduction & Converting wind energy to electrical energy with induction generators & Cleaner energy, decentralized generation. \\ \hline \multirow{4}{*}{Hydro Energy [33]} & \multirow{4}{*}{24 h} & Peak clipping & Pumped hydro: Water pumped during off-peak hours generates electricity during peak hours. & Emergency power, cleaner energy, small centralized power generation. \\  & & Load switching & Small hydro: Decentralized energy, small centralized power generation. \\  & & Flexible load growth & & \\  & & Flexible load growth & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{Waste-to-Energy [34; 35]} & \multirow{4}{*}{24 h} & Peak clipping & Biogas: Anaerobic digestion of biodegradable waste into methane produces energy. & Cleaner energy, small centralized power generation, less carbon production. \\  & & Load shifting & & & \\  & Valley filling & Thermal: Combustion of waste to produce energy. & Cleaner energy, small centralized power generation, less carbon production. \\ \hline \multirow{4}{*}{ESS [36]} & \multirow{4}{*}{24 h} & Peak clipping & Energy is stored at off-peak hours in various systems such as electric springs, pumped hydro, fuel cells, hydrogen cells, supercapacitors, etc. & Emergency power, cleaner energy, small centralized power generation, less carbon production, charging stations. \\ \hline \multirow{4}{*}{Vehicle-to-Grid [37]} & \multirow{4}{*}{24 h} & Peak clipping & Energy is stored at off-peak hours in various systems such as electric springs, pumped hydro, fuel cells, hydrogen cells, supercapacitors, etc. & Emergency power, cleaner energy, small centralized power generation, less carbon production, charging stations. \\ \hline \multirow{4}{*}{Vehicle-to-Grid [37]} & \multirow{4}{*}{24 h} & Peak clipping & EV charging in off-peak hours can provide power to grid-like ESS at the time of need. & Emergency power, cleaner energy. \\  & & Flexible load growth & & \\ \hline \multirow{4}{*}{Geothermal Energy [38]} & Available when water is in contact with lava & Peak clipping Load reduction & Providing intermittent boosts to power levels. & Small centralized power generation, spinning reserve. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different types of DERs with DSM applications.
## Abbreviations  
\begin{tabular}{l l} Symbol & Explanation \\ P\({}_{z}\) & File bearing capacity \\ ML & Machine learning \\ XGBoost & Extreme gradient boosting \\ AdaBoost & Adaptive boosting \\ RF & Random forest \\ DT & Decision tree \\ SVM & Support vector machine \\ ANN & Artificial neural network \\ ANFS & adaptive neuro-fuzzy inference system \\ GA & Genetic algorithm \\ BPNN & Backpropagation neural network \\ GBDT & Gradient boosting decision tree \\ LightGBM & Light gradient boosting machine \\ DLNN & Deep Learning Neural Network \\ PSO-ANN & Particle swarm optimization--ANN \\ GPR & Gaussian process regression \\ \(R^{2}\) & Coefficient of determination \\ \(MAE\) & Mean absolute error \\ \(MSE\) & Mean square error \\ \(RMSE\) & Root mean square error \\ \(MARE\) & Mean absolute relative error \\ \(NSE\) & Nash-Sutic/diffee model efficiency \\ \(RSR\) & Relative strength ratio \\ SPT & Standard penetration test \\ CPT & Cone penetration test \\ D & Diameter \\ X\({}_{1}\) & Depth of first layer of soil embedded \\ X\({}_{2}\) & Depth of second layer of soil embedded \\ X\({}_{3}\) & Depth of third layer of soil embedded \\ X\({}_{p}\) & Pile top elevation \\ X\({}_{p}\) & Ground elevation \\ X\({}_{4}\) & Extra pile top elevation \\ X\({}_{m}\) & Pile tip elevation \\ N\({}_{5}\) & SPT blow count at pile shaft \\ N\({}_{n}\) & SPT blow count at pile tip \\ \end{tabular}
# Impact Evaluation of Track Girder Bearing on Yeongjong Grand Bridge
## 4 Finite Element Analysis
### Behavior of the Track Girder According to the Bearing Type  
To analyze the structural behavior of the track girder according to the bearing type using the numerical model applied to the existing line bearing and ECB, the behavior of the track girder according to the train speed was analytically derived and compared with the field measurement results. To accurately reflect the direct-fixation track girder of the Yeongjong Grand Bridge, the track girder and the vertical stiffener were modeled as 3D shell elements, and the rails, crossbeams, and bracings were modeled as beam elements, as shown in Figure 16. MIDAS Civil [19], a general structural analysis program, was used for finite element analysis. Figure 14: Results of finite element analysis (vertical load). (a) Line bearing; (b) ECB. Figure 15: Results of finite element analysis (horizontal load): (a) line bearing; (b) ECB. The boundary conditions were set as listed in Table 4, and the general link was used to apply the direction of bearing behavior, axial rigidity, and damping effects to identify the characteristics of the existing line bearings and ECBs. For the load condition, to apply the load of the train acting on the track girder of the Yeongjong Grand Bridge, the time history function was modeled and applied to the time history analysis, considering the effects of the moving loads of Korea Train eXpress, (KTX) train (wheel load and lateral force) and according to the train speed variations, as shown in Figure 17. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Line Bearing** & **ECB** \\ \hline Bearing input rigidity & \(\text{D}_{\text{s}}=1.0\times 10^{4}\) kN/mm & \(\text{D}_{\text{s}}=2.4\times 10^{5}\) kN/mm \\ \hline Bearing member configuration & Vertical load support: steel Horizontal load support: steel & Vertical load support: polyurethane horizontal load support: steel \\ \hline Damping ratio & 0.02 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Boundary conditions. Figure 16: Numerical modeling of the track.
# An Autonomous Thermal Camera System for Monitoring Fumarole Activity
## 3 Deployment
### \"Oldbutot\" Lava Field  
Figure 5 shows data availability and camera parameters recorded at the \"Oldbutot\" lava field. After the installation, the camera recorded hourly data until March 2020. When the enclosure was opened during maintenance in July 2020, it was found that some power cables within the enclosure had come loose. The cause for this unforeseen damage has not been found. After replacing damaged cables and the control unit, the system functioned until the end of December and resumed working again in February 2021 after replacing a failed battery. The temperature recorded by a sensor at the camera lens varies between 288 K and 305 K (Figure 4(b)). A difference with the camera at the gorge is that the upper temperature is on average \(\pm\)10 K lower, and there seems to be a seasonal variation.  
The readings in the control unit started after replacing the control unit in July 2020. For unknown reasons, the logging failed soon after despite the camera continuing to function; the readings resumed in March 2021. The temperature of the Raspberry Pi (Figure 4(c)) seems precise as the smaller fluctuations match that of the camera temperature (Figure 4(b)). There is a difference in accuracy, where the Raspberry Pi temperature reading gradually increases over time. Furthermore, in June 2021, there was an unexplained deviation in the Raspberry Pi temperature readings which is not reflected in the camera temperature readings. The battery status shows that the lower voltage limit increased over time (Figure 4(d)), reflecting a continuous improvement in the battery condition.  
During the 2022 maintenance, the solar panel appeared slightly corroded. This may be a result of aggressive exhalations coming from the nearby fumarole. However, due to the overcapacity of the panel, there is no concern about the power status in the short run, and the battery voltage remains excellent (Figure 4(d)). The germanium window of the enclosure did not show any signs of precipitates. The inside of the housing was clean and no corrosion was visible, suggesting that the airtight seal made of putty works as expected.  
Figure 4: Camera system parameters at the “OI Njorowa” gorge. From top to bottom, the records of (**a**) data availability and (**b**) camera lens temperature, available since the installation of the camera in July 2019, and the records of (**c**) core temperature of the Raspberry Pi in the control unit and (**d**) the battery voltage at the Arduino, measured as of July 2020.
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments
### Visualization and Qualitative Analysis
In addition to the overall performance, we compare the success rates of different trackers on eleven challenging attributes of the OTB dataset, and the results are shown in Figure 8. It can be seen from Figure 8 that our FETrack shows an extremely competitive performance in most challenging tracking scenarios compared with other transformer-based one-stream trackers, such as OSTrack, GRM, and SeqTrack. Notably, compared with other state-of-the-art methods, our FETrack achieves the best success rate on the attributes of In-Plane Rotation, scale variation, Deformation, and occlusion, obtaining success rates of 71.2%, 73.1%, 67.5%, and 69.8%, respectively. This is attributed to the fact that we incorporate the independent template stream into our FETrack to ensure the quality of the template features and combine the template-search stream to capture appearance changes of the target. In addition, our FETrack also achieves the best overall performance in evaluating all the attributes comprehensively. The above results further validate that the proposed FETrack possesses excellent adaptability and robustness. Figure 7: Visual comparisons between our FETrack and other state-of-the-art trackers on four representative sequences (i.e., bird-2, crab-3, glraffa-2, and kite-10) of the LaSOT dataset. Different color rectangular boxes are used to mark the results obtained by different trackers, and the frame numbers are displayed in the upper left corner of each frame. These results validate the robustness of our tracker against challenges such as scale variation, similar targets, and occlusion.
# Benchmarking Deep Learning Models for Instance Segmentation
## 4 Review Method  
We divided the target methods into accuracy-focused and speed-focused models. Mask R-CNN [4] is the most basic model in the field of instance segmentation. It was therefore selected as a baseline accuracy-focused model for comparison with the other models. SORR [3] and Dual-Swin-L [2] were chosen to represent the accuracy-focused models because the Transformer structure has been successfully applied in the field of instance segmentation. CenterMask [5], YOLACT [8], YolactEdge [9], BlendMask [7], and SOLOV2 [6] were designed for real-time use and provide a trained model; hence, they were used to exemplify the speed-focused models.
# Towards a Universal Semantic Dictionary
## 4 Experimental Setup
### Dataset Creation from PanLex  
The procedure applied for extracting a proper data from the PanLex database for training multilingual embedding models roughly follows the same steps as in [6]. After extracting the raw translation pairs form the PanLex database, a filtered version of entries was formed by dropping translations with a confidence value below 7 and those for which no word vector was found in the _fastText_ embedding. This resulted in an English-Italian word translation set containing 69,623 entries.  
For the test set, 1500 English words were taken and split into five frequency bins, 300 randomly assigned to each bin. The bins were defined as in [6], i.e., in terms of rank in the frequency-sorted lexicon: [1K-5K], [5K-20K], [20K-50K], [50K-100K], and [100K-200K]. In [5], the word vectors sorted by their frequency in descending order were published, and this order was used as the source of English word frequency data. In the PanLex database it is a common issue that one English word has sometimes as many as 10 different Italian translations. Therefore, to avoid having an undesirably huge test set with many Italian synonyms only those English words were selected, for which in the corresponding bin only one Italian translation was present. This way the obtained test set contains exactly 1500 word pairs, which are made up of 1500 different English words and their Italian translations.  
For the training set, the 69,623 entries were first sorted by their English frequency, then the top 5K entries were extracted and, as in [6], care was taken to avoid any overlap with test elements on the English side. Then, the top 5K entries were selected in three different ways:  
1. The first 5K entries were taken.
2. The first 5K different English words were taken with the most frequent Italian translation.
3. Only those English words were taken for which only one Italian translation was present.  
\begin{table}
\begin{tabular}{l l r} \hline \hline
**English** & **Italian** & **Confidence Values** \\ \hline Sarajevo & Sarajevo & 9 \\ euro & euro & 9 \\ \hline simple & semplice & 8 \\ difficult & difficile & 8 \\ \hline college & università & 7 \\ plausible & verisimile & 7 \\ \hline sea & mare & 6 \\ sky & cielo & 6 \\ \hline better & meglio & 5 \\ inform & informare & 5 \\ \hline combustible & combustibile & 4 \\ office & ufficio & 4 \\ \hline sorcerer & consciore & 3 \\ it & ella & 3 \\ \hline Great Wall of China & Grande muraglia cinese & 2 \\ factory workers & lavoratori dell’industria & 2 \\ \hline stay & restate & 1 \\ sometimes & qualche volta & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample of PanLex entries of the extracted tsv file.
# Abstractive Sentence Compression with Event Attention
## 5 Experiments  
### Experimental Setting  
The proposed model is evaluated with three kinds of datasets: MSR dataset [21] (see Supplementary Materials), [PERSON] dataset [9] (see Supplementary Materials), and Korean sentence compression dataset. The simple statistics on each dataset is given in Table 2. The MSR dataset proposed by [PERSON] et al. is used for abstractive sentence compression. It consists of a newswires, business letters, journals, and technical documents from the Open American National Corpus, and the compressed sentences are created manually. In this dataset, the numbers of training, validation, and test examples are 21,145, 1908, and 3370 pairs, respectively. The average length of source sentences is 193.2, while that of target sentences is 133.6. [PERSON] dataset [9] is used for deletion-based sentence compression. This dataset is automatically produced from Google News using the method proposed by [PERSON] et al [6]. The total number of sentence pairs is 10,000, and target sentences have 13.7 fewer words than source sentences on average. The last Korean sentence compression dataset is designed for testing morphologically complex languages. This dataset contains 3117 source sentences from Korean news articles, and the sentences are split to 8:1:1 for training, validation, and test sets, respectively. Its target sentences are created by a native speaker and their length is short on average by 6.01 words when compared to the source sentences.  
For the experiments below, the dimension of word embeddings is set as 256, and that of all hidden layers is set as 512. LSTM [33] is used for the function \(f\) in Equation (3) and the hyperbolic tangent is used for the function \(g\) in Equation (5) and (6). The proposed network is trained with batch size 64 and learning rate 0.001, and is optimized with an Adam optimizer [38]. The value of \(\lambda\) in Equation (7) is 0.6 which is estimated using MSR validation set. BLEU [23], ROUGE [22], and compression ratio (CR) [24] are used as evaluation metrics, where **CR** is computed as  
\[\textbf{CR}=\frac{1}{u}\sum_{i=1}^{u}\frac{\text{No. of tokens in the $i$-th compressed sentence}}{\text{No. of tokens in the $i$-th source sentence}},\]  
and \(u\) is the number of sentences in a test set. Since the test set consists of pairs of a source sentence and its target sentence, the golden compression ratio can be computed from the test set. The golden compression ratio on MSR dataset is 68.60%, while those on Filippova dataset and Korean dataset are 42.62% and 47.52%, respectively. Two baselines are adopted for comparing the proposed model with existing models. One baseline is the standard sequence-to-sequence model proposed by [PERSON] et al. [16], and the other is the pointer generator [19] in which the copy technique is applied to the sequence-to-sequence model.
# A Simple Model of Ballistic Conduction in Multi-Lead Molecular Devices
## 7 Connections with the Meir-Wingreen Formula  
The Meir-Wingreen (MW) formula gives an exact expression for the current through a molecular device described using a correlated molecule but with leads and lead-molecule interactions treated in the one-electron approximation. In previous work [70], we showed that the SSP method is consistent with the MW formula, in the elastic-scattering limit when the electron-phonon interactions are neglected (i.e., in the Born-Oppenheimer approximation). SSP produces identical formulae for the transmission, including the case in which the description of the central molecule includes electron correlation.  
The transmission in the SMSP approach is determined by boundary conditions on a device wave function with conceptual advantages in the chemical interpretability of the internal molecular channels for conduction. These channels are determined by the attached and ionised states appearing in the Lehmann representation of the equilibrium molecular GF. The properties of these channels are defined in terms of the characteristics of the DOs associated with each of the GF poles. The sets of orbitals and poles define spectral expansions of structural polynomials used to formulate the expressions we derived in this paper. The relevant formal properties of the polynomials are retained regardless of the level of theory, from Huckel, through HF, to sophisticated GF formalisms. The molecular part of the problem may therefore be treated with empirical, semi-empirical, or ab initio methods, with or without the inclusion of correlation, thus giving a seamless single formalism to describe conduction through molecular devices.  
At any of these levels of approximation, the inverse \((\mathbf{P})^{-1}\) of the SSP/SMSP device matrix is the device GF (Equation (85) of [70]). The poles of this matrix are complex-valued and represent device resonances corresponding to the presence of an extra electron (or hole) in the sea of (possibly correlated) electrons. The imaginary parts of these poles are inversely related to the lifetimes of the resonances. These lifetimes represent transit times for the particles or holes crossing the device, times that are particularly short for Koopmans poles but long for shake poles. In the SMSP context, it can be seen that the poles of the device GF can be obtained by solving the equation \(|\mathrm{D}_{\mathrm{L}}(E)|^{2}=0\). Each complex conjugate pair of poles is then associated with a Dyson orbital and (real) pole energy of the molecular GF. Hence, the SMSP model gives a direct route to calculation and interpretation of all the properties that can be accessed through GF methods.
## 2 Literature Review
### Communication Technologies for IoT Healthcare  
Efficient communication protocols are fundamental to IoT-driven healthcare systems, ensuring real-time monitoring, secure data transmission, and minimal latency for many IoT-driven healthcare applications in order to guarantee real-time monitoring and timely alerts. For example, rapid data transmission is necessary for emergency response apps, smart wearables, and remote patient monitoring systems to identify critical medical problems like hypoglycemia or cardiac arrhythmias.