esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data  
Driving data, used in this experiment, were collected flux tower site (68.740\({}^{\circ}\) W, 45.204\({}^{\circ}\) N) in Howland forest from 2002-2006, Maine, USA, and the vegetation was mainly evergreen needle leaf forest (ENF), and the climate was warm summer continental. The vegetation of the old evergreen needle leaf forest was red spruce (_Pinus rubens Sarg_), eastern hemlock (_Tsuga canadensis ([PERSON]_) and other conifers. The mean annual temperature (MAT) is 5.27 \({}^{\circ}\)C and the mean annual precipitation (MAP) is 1070 mm [37; 47; 48]. The flux dataset contained daily ecological and environmental data from 2000 to 2006, such as photosynthetically active radiation (PAR), air temperature (\(T_{a}\)), and relative humidity (RH). In addition, the flux and ecological data, such as net ecosystem carbon exchange (NEE), gross primary productivity (GPP), leaf area index (LAI), and ecosystem respiration (\(ER\)) were collected from the AmeriFlux ([[http://ameriflux.lbl.gov/sites](http://ameriflux.lbl.gov/sites)]([http://ameriflux.lbl.gov/sites](http://ameriflux.lbl.gov/sites))). The observations of Howland forest flux site are comprehensive and of high quality, so a large number of ecological models, model-data fusion andremote sensing studies selected the site as a case [28; 37; 48; 49]. This study also selected this flux site for parameter sensitivity and uncertainty research of the forest ecosystem model.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 3 Experiment
### Model Evaluation and Analysis
#### 3.3.4 Comparative Analysis  
To further evaluate the performance of the ResNet1D-AttLSTM model, this study conducted comparative experiments, comparing it with trajectory classification models based on FishNet [31], pGRU [32], and CNN-LSTM [34] network architectures. The selection of these models was based on their methodological relevance to our work, as they employ deep learning architectures for ship trajectory classification, to process trajectory data as a sequence task without converting it to an image, and to address similar maritime application scenarios. Among these, FishNet employs a one-dimensional convolutional neural network architecture, while pGRU represents an advanced recurrent neural network variant that achieves efficient modeling of low-dimensional heterogeneous data by partitioning each input feature within the activation function and assigning independent parameters to each partition. The comparative experiments used accuracy as the evaluation metric, and the results are presented in Table 5 and Figure 7.  
According to the data in Table 5 and Figure 7, the ResNet1D-AttLSTM model outperforms all other models in the trajectory classification task, achieving an accuracy of 89.7%, which is significantly higher than the others. This model integrates a one-dimensional residual network with an attention-based long short-term memory network, enabling it to effectively capture long-term dependencies in ship trajectory sequences and learn key features through the self-attention mechanism. In contrast, the accuracies of FishNet and pGRU are 81.4% and 82.2%, respectively, while that of CNN-LSTM is 84.1%. These results indicate that the ResNet1D-AttLSTM model exhibits superior accuracy and robustness when handling complex trajectory data.
## 3 Results  
### Fabrication of TGBC Structure Organic Complementary Integrated Circuits and OTFT Characteristics  
The fabricated complementary integrated circuits have a top-gate bottom-contact structure in p-type and n-type OTFTs (Figure 2a). Figure 2b shows a microscope image of the fabricated complementary organic inverter circuit. In previous research, we adopted a stacked device structure to surface-functionalized the source/drain electrodes of p-type and n-type OTFTs with SAM materials separately [15; 16; 17; 18; 19]. However, this led to an increase in the number of device manufacturing steps and a reduction in yield. In this research, a complementary organic integrated circuit was realized by adapting a p-type D-A polymer semiconductor [21; 22; 23; 24], which can obtain transistor characteristics even if it is modified with SAM material for an n-type semiconductor for the source/drain electrodes of the p-type OTFT. As a result, the complementary integrated circuit of this TG structure reduced the number of steps from 14 (in previous research) to seven, and succeeded in developing a simple process. The effect of treatment using the SAM material on a printed electrode was evaluated by photoelectron yield spectroscopy (PYS) in air. The work function of the untreated electrode was 4.8 eV, which was changed to 4.0 eV by 4-MBT treatment [18]. The transfer (Figure 2c) and output (Figure 2d) characteristics of the fabricated OTFTs that make up the integrated circuit were measured using a semiconductor parameter analyzer in ambient air conditions. The channel length (L) and width (W) ratio of the p-type and n-type OTFT devices was L/W = 29/960 \(\upmu\)m. These OTFT devices exhibited excellent electrical performance at relatively low operating voltages of 10 V. For the p-type OTFT, the estimated hole mobility and threshold voltage were 0.11 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and 0.09 V, respectively, and the on/off ratio was greater than 10\({}^{5}\). For the n-type OTFT, the estimated electron mobility and threshold voltage were 0.19 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and 1.40 V, respectively, and the on/off ratio was greater than 10\({}^{7}\). These values were the averages of the four devices. Additionally, these advantageous output characteristics suggest that the contact resistance for each TFT device type is low. In many reports, there is a large difference in mobility between p-type and n-type OTFTs, and characteristics matching those of the OTFTs constituting the inverter circuit were required. This matching is important not only for correcting the switching point of the inverter characteristic, but also for correcting the deviation of the rise and fall time. However, in this study, since p-type and n-type OTFTs have almost equivalent mobility and threshold voltage, it is possible to realize a circuit with the same balance.
## 6 Results and Analysis
### Error Analysis  
Our proposed approach searches the dictionary against the user input word(s). Spelling mistakes and different blank spaces result in a fail, no match, in the dictionary. Although the test corpus is pre-processed, it contains many irregular words and compound words which cause a problem for word boundary identification during the pre-processing, e.g., words are \(\mathbf{\texttt{\tiny{2}}}\), \(\mathbf{\texttt{\tiny{3}}}\), \(\mathbf{\texttt{\tiny{4}}}\) etc., These entries in the test dataset reduce the system's accuracy because such junk entries and compound words should not be present in the proposed dictionary. The unavailability of exact words leads to no match in the proposed dictionary, and decrease in performance. Another reason for the poor performance of the proposed lemmatizer concerns proper nouns names of persons, places, and things. This also reduces the performance of the proposed approach as it seems impossible to cover all the entity names in the proposed dictionary.  
Table 7 shows the statistical details of the error analysis for the with-PoS-DLA approach concerning single and multi-word expressions. A total of 2852 words were incorrectly lemmatized, out of which 65% were single-words, and 35% were multi-word expressions. The portion of single-word expressions is significant in this approach because of the PoS tags assigned during the annotation process. When we use single words with their respective PoS tags, it might be possible that the word is present in a dictionary with another PoS tag. Therefore, this mismatch causes reduction in the performance of the proposed system.  
Table 8 shows the statistical details of error analysis for the with-PoS-DLA approach concerning PoS tags. A total of 2852 words were incorrectly lemmatized, out of which 36% were words having noun tags (NNs), 11% were proper nouns (PNs), 13% were case maker words (P), 8% words were personal pronouns (PPs), 13% were verbs (VBs), 7% were adjectives (ADJa), and 8% were aspectual auxiliaries (AAs). The remaining 4% of the words had other tags. The portion of words with noun tags (NNs) is significant because of the PoS tags assigned during the annotation process. The user-assigned tagset on the test dataset may be different from the USAS PoS tag (the present tag in the dictionary for the respective word) and this cause a mismatch. Consequently, it reduces the performance of the proposed system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Accuracy** \\ \hline With PoS-DLA & **66.79\%** \\ \hline Without PoS-DLA & **76.44\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy obtained by proposed Urdu lemmatizer.
# Solution Extrusion Additive Manufacturing of Biodegradable Polycaprolactone
###### Abstract  
Polycaprolactone (PCL) is a resorbable semicrystalline polymer that degrades slowly via hydrolysis and has applications in medical implants and drug delivery. As a result of its low melting point, PCL can be processed easily by conventional polymer processing techniques. However, the additive manufacturing of PCL remains a challenge, mainly due to the fact that there are no commercially available filaments for traditional fused deposition modeling (FDM). Furthermore, when the materials are fabricated via FDM for drug delivery applications, the high temperature may deactivate the incorporated drugs/biomolecules. This study investigates the solution extrusion additive manufacturing of PCL using a lab-developed solution-type device. The device comprises a solution extrusion feeder, driving stepper motors, a power source, a syringe equipped with a dispensing tip, an accumulation platform, and a control interface. The influences of different manufacturing parameters on part quality were evaluated. The experimental results suggest that the tensile strength of the additively manufactured parts increases with fill density but decreases with the ratio of PCL to dichloromethane (DCM) and moving speed of the dispensing tip. Parts fabricated by 90\({}^{\circ}\) print orientation of infill exhibited the greatest mechanical strength. The fabricated parts tend to heal the gaps among strips after additive manufacturing, but tiny pores can still be seen on the surfaces.  
polycaprolactone; additive manufacturing; solution extrusion; manufacturing parameter
# Study of the Photo-Response of Doped GaAs with Aging
## 3 Results and Discussion
They obtained the following values for the V\({}_{\rm Ga}\) in its different states of charge: 0.100 eV for charge (\(-\)1), 0.068 eV for charge (\(-\)2) and 0.051 eV for charge (\(-\)3). On the other hand, for V\({}_{\rm As}\) they obtained the values: 0.145 eV for charge (\(-\)1), 0.088 eV for charge (\(-\)2) and 0.042 eV for charge (\(-\)3). The approximate calculation carried out with Equation (6) yielded values very similar to those reported by [PERSON] and [PERSON], in particular for the charge (\(-\)2) and (\(-\)1) of the As vacancy, which is the vacancy that has the highest probability to be formed, because the films grew rich in Ga. An inspection of the position of the maximum peaks in the spectra of Figure 3 gives a difference of 0.052 eV, very close to the difference between the As vacancy energy in the charge state (\(-\)2) and the state (\(-\)1). Furthermore, the DT, DF and DO peaks in Figure 1 are located at 0.066 eV, 0.101 eV and 0.180 eV, respectively, with respect to the energy gap of GaAs at 300 K (1.424 eV). These values do not give as close results as the previous ones when compared to the charge states of the deep trap energy level of the As vacancy. However, if we subtract the LO phonon energy (0.030 eV) for GaAs from each one, the values we obtain are 0.036 eV, 0.071 eV and 0.150 eV, which are quite close to the trap energies for the charge values (\(-\)3), (\(-\)2) and (\(-\)1) for the As vacancy. Subtracting the phonon LO is justified because vacancies deform the lattice in what is called the Sach-Teller deformation, and therefore, an electron that transits from the conduction band to the vacancy level loses thermal energy due to the rearrangement of the lattice. It can be stated that the aging process of our GaAs samples is due to the oxygen in the air that produces vacancies due to the oxidation of Ga and mainly As. These vacancies begin in a charge state of (\(-\)3) over the years, and they change their state of charge up to (\(-\)1) by self-compensation. The aging effect is mainly manifested in the undoped samples, whereas in the doped samples, the impurities Ge and Sn prevent the formation of As vacancies since these impurities occupy Ga and As sites because these elements are amphoteric.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.1 PPPM Analysis of k-Anonymity (Q1.1.1)  
In this section, we present the quantitative results of applying the \(k\)-anonymity model on our real-life event logs under two different aspects as result and time utility qualities.  
Result Utility Quality  
The Figure 4 heatmap shows the applied \(k\)-anonymity model for PM quality metrics with respect to five different \(k\) parameter values behavior on our real-life event logs. For all the results, baseline metric values without any anonymization were included in Table 3 and employed for the comparative interpretations. Since \(k\) values are proportional to privacy protection, and the runtime performance is very low (>30 min timeout limit), this model does not apply to the Road Traffic Fines event log for \(k\) < 16 values.  
All \(k\)-anonymized event logs showed alignment with their baseline fitness and generalization metric values. However, for all event logs except for BPIC13, there was an increase in precision for a strong privacy budget in comparison with their baselines. On the other hand, CoSeLog event log generalization was also reduced in this case.  
Time Utility Quality  
Figure 5 includes the runtime (s) performance metric results of the applied \(k\)-anonymity model on our real-life event logs. During the experimentation with the \(k\)-anonymity algorithm, an inversely proportional decrease in model runtime was observed with increasing privacy levels as a consequence of specific \(k\) parameter modifications, attributable to reduced model complexity due to the instance count size. The Road Traffic Fines event log had the highest runtime performance even for the lowest \(k\) value.
## 2 Background
### Federated Learning (FL) for Data Sharing in the IoMT  
As depicted in Figure 2, federated learning (FL) operates as a collaborative machine learning framework in which multiple edge devices jointly develop shared predictive models without having to exchange their private datasets.  
Figure 1: Data sharing in IoMT.  
Consider a network of \(N\) distributed nodes represented by \(\{C_{1},C_{2},\cdots,C_{N}\}\), each possessing a distinct local training dataset \(D_{i}\), \(i=\{1,2,\cdots,N\}\). These participants aim to collaboratively optimize a shared global model \(w\) while maintaining strict data confidentiality for their respective information sources. During the \(t^{\text{th}}\) training cycle, the FL process unfolds through three sequential stages:  
Step I: The central server distributes the updated global model \(\mathbf{w}^{(t-1)}\) to all participating devices through network transmission.  
Step II: Every participating device develops an individual model through training on its own dataset. In particular, the device \(C_{i}\) solves an optimization problem \(\mathbf{w}_{i}^{(t)}=argmin\ L(D_{i},\mathbf{w}^{(t-1)})\) to update the local model \(\mathbf{w}_{i}^{(t)}\) using stochastic gradient descent, where \(L(\cdot)\) is the loss function. These edge devices subsequently transmit their local model updates \(\mathbf{g}_{i}^{(t)}=\mathbf{w}^{(t-1)}-\mathbf{w}_{i}^{(t)}\) to the central server.  
Step III: The server synthesizes these distributed parameters through aggregation algorithms to formulate an enhanced global model \(\mathbf{g}^{(t)}=\sum_{i=1}^{n}\frac{|D_{i}|}{\mathbf{S}}\mathbf{g}_{i}^{(t)}\), where \(|D_{i}|\) is the size of the \(i^{\text{th}}\) device's local training dataset, and \(S\) is the total number of training examples. Next, the server updates the global model via  
\[\mathbf{w}^{(t)}\leftarrow\mathbf{w}^{(t-1)}-\eta\cdot\mathbf{g}^{(t)}, \tag{1}\]  
where \(\eta\) is the global learning rate. This global model is then redistributed to all connected devices for subsequent optimization cycles.
## 2 Materials and Methods
### Calibration and Validation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & & & & **[PERSON] et al.**[13] & \multicolumn{2}{c}{**This Study**} \\ \hline
**Variables** & **Symbol** & **Equation** & **Units** & **Maize** & **Soybean** & **Maize** & **Soybean** \\ \hline Derived through iterative process: & & & & & & & \\ Maximal light use efficiency & \(\epsilon_{0}\) & (8) & g C mol\({}^{-1}\) & 0.526 \(\pm\) 0.007 & 0.374 \(\pm\) 0.005 & 0.573 \(\pm\) 0.002 & 0.407 \(\pm\) 0.002 \\ Sensitivity of to diffuse light & \(\beta\) & (11) & unless & 0.347 \(\pm\) 0.051 & 0.411 \(\pm\) 0.065 & 0.181 \(\pm\) 0.014 & 0.294 \(\pm\) 0.020 \\ Water stress curvature parameter & \(\sigma_{\rm{Wooler}}\) & (14) & kPa & 6 \(\pm\) 0.25 & 4 \(\pm\) 0 & 6 \(\pm\) 0 & 4 \(\pm\) 0 \\ Phenology curvature parameter & \(\sigma_{\rm{Polar}}\) & (15) & m\({}^{2}\) m\({}^{-2}\) & 18 \(\pm\) 4.59 & 18 \(\pm\) 7.15 & 7 \(\pm\) 0 & 8 \(\pm\) 0 \\ Calculated & & & & & & \\ Maximal green leaf area index [irigated] & gLA\({}_{\rm{max}}\) & (10), (15) & m\({}^{2}\) m\({}^{-2}\) & 4.93 [6.78] & 4.63 [6.15] & 5.04 [5.14] & 3.86 [4.25] \\ Constants & & & & & & \\ Light extinction coefficient & k & (10) & unitless & 0.443 & 0.601 & 0.443 & 0.601 \\ Minimum temperature for physiological activity & \(T_{\rm{min}}\) & (1), (13) & “C & 10 & 10 & 10 & 10 \\ Maximum temperature for physiological activity & \(T_{\rm{max}}\) & (1), (13) & “C & 48 & 48 & 48 & 48 \\ Optimal temperature for physiological activity & \(T_{\rm{opt}}\) constant & (13) & “C & 28 & 28 & 28 & 28 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters for the calculation of scalars for the cloud-adjusted light use efficiency model (LUE\({}_{\rm{C}}\)). The parameters in the upper portion of the table were calculated through the iterative process while those in the lower portion of the table are model constants from [PERSON] et al. [13]. * **LUEc Validation**. The estimated daily GPP values were compared to the observed AmeriFlux determined daily GPP using linear regression (\(\alpha\) = 0.05), with corresponding R\({}^{2}\), root mean square error (RMSE), and mean normalized bias (MNB).
# ER Morphology in the Pathogenesis of Hereditary
## 1 Clinical Features of HSP  
HSP comprises a group of clinically and genetically heterogeneous neurodegenerative disorders caused by a broad range of mutations affecting the so-called SPG genetic loci. HSP is rare, showing an overall estimated prevalence of about 1-10/100,000 [1]. However, its distribution is highly variable among different regions (also due to discrepancies in the classification and diagnosis). HSPs share as a common feature a bilateral spasticity and weakness of the lower-extremities, as emerging by neurological examination [2]. The severity of these symptoms varies among patients, and they are often accompanied by lower-extremity hyperreflexia and extensor plantar responses.  
The HSPs are progressive disorders whose hallmark is the degeneration of the cortical motor neurons that project axons to the spinal cord. Loss or damage of axons is evident by radiological examination in the corticospinal tract, although they are not exclusively confined to this region. Indeed, the corpus callosum, frontal and temporal lobes, cerebellum, as well as other brain regions display imaging changes [3], indicating the involvement of a broad range of neuronal subtypes. Although there is an indication that radiological defects correlate with severity and progression of the disease, the association with the genotype of the patients is still weak [3].  
Clinically, HSPs have been classified as \"pure\" (or uncomplicated) HSPs when symptoms are limited to lower limb weakness and spasticity, likely arising from degeneration of the corticospinal tract, or as \"complicated\" HSPs when patients present other symptoms such as cerebellar ataxia, peripheral neuropathy, optic atrophy, seizures, and dementia. Complicated symptoms likely derive from dysfunctions of additional brain regions and neuronal types. Although these symptoms often correlate with the specific genotype of the patient, they usually complicate the diagnosis due to overlap with signs and symptoms of other neurodegenerative diseases (e.g., ataxias, dementias, amyotrophic lateral sclersis) [4]. This may also suggest that HSPs are not a single disease but a group of different diseases whose common feature is degeneration of the corticospinal tracts.  
Symptom onset is also highly variable, ranging from early childhood (classified as \"early onset\") to later age. Early onset HSP are most often characterized by non-progressive symptoms, while later onset HSPs usually worsen slowly but unceasingly. Although HSPs are disabling, the life span of patients is not shortened. No cure is available to prevent or reverse nerve degeneration in HSP and current treatments simply target symptom reduction [3].