esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
#### 4.3.4 Analysis of Local Transformer  
In the task of continuous sign language recognition, each gloss is associated with a specific segment in the continuous sign language video. To augment the model's ability to learn local information within the sequence, we incorporated a local Transformer prior to the output layer. In order to compare its performance with 1D-CNN and examine the optimal size of the local window, we conducted experiments to investigate the differences in performance across various window sizes on PHOENIX-2014.  
Based on the experimental results, it is evident that the absence of a network with local perception capabilities leads to a highly unsatisfactory final outcome. This finding underscores the significance of local temporal perception for cSLR tasks. The integration of a 1D-CNN contributes to an approximate reduction of three in the WER metric, as it leverages a smaller convolutional kernel for processing local regions within extended sequences, ultimately supplying local information for the final output. Utilizing a local Transformer further diminishes the WER, which can be ascribed to the 1D-CNN's shared convolutional kernel that executes convolution operations using identical parameters on varying local information, thus yielding constrained information extraction. Conversely, the self-attention mechanism of the Transformer facilitates the dynamic assimilation of local sequences, demonstrating a more adaptable and resilient processing approach capable of capturing an abundance of local information. On the other hand, in Table 4, for the 1D-CNN, a larger convolutional kernel contributes to improved performance. However, with regards to the local Transformer, setting the local perception size to 5 yields the best results. If the size is too small, it may not be able to perceive sufficient information, whereas if it is too large, the performance might be adversely affected by the actions corresponding to adjacent glosses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Spatial Features**} & \multirow{2}{*}{**Attention**} & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\ \cline{3-6}  & & **del/ins** & **WER** & **del/ins** & **WER** \\ \hline \multirow{2}{*}{Coarse-grained features} & scaled-dot & 12.3/4.8 & 28.3 & 12.7/4.1 & 28.5 \\  & Euclidean & 12.0/3.6 & 27.1 & 11.8/3.6 & 27.0 \\ \hline \multirow{2}{*}{Fine-grained features} & scaled-dot & 16.1/2.9 & 34.7 & 15.3/3.5 & 34.5 \\  & Euclidean & 13.5/4.0 & 32.9 & 14.1/3.7 & 33.4 \\ \hline \multirow{2}{*}{**Fused features**} & scaled-dot & 8.7/3.4 & 26.3 & 8.4/4.2 & 25.9 \\  & **Euclidean** & **7.4/3.3** & **22.4** & **7.3/3.2** & **22.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of different attention weight calculation methods on PHOENIX-2014.  
Figure 7: An example of attention weight visualization for a Transformer utilizing Euclidean distance-based metrics. The sample data used in this figure are consistent with those in Figure 4. It is evident that the use of Euclidean distance significantly alleviates the phenomenon of attention sparsity.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 4 Results
### Does the Synthetic Data Resemble the Properties of Real Data?
#### 4.1.2 Is the Generated Text Semantically Coherent?  
To get a better understanding of the quality of generated text, we manually inspected random documents of the synthetic corpora (examples in Figure 6 and Appendix C). We make the following observations: while most texts are syntactically correct, the majority is incoherent. We hypothesize that the incoherence is caused by the large variety of reporting styles in the training corpus. This may have inhibited the language models to learn a specific type of text more accurately. Furthermore, we observe some replication of templates and phrases of real documents. An example of this is shown in Figure 6. This was most evident for texts generated by the GPT-2 with beam search. We give additional examples in Appendix C where we used the same prompt to generate text with all four approaches. In those examples, the LSTM texts are more varied, but also less coherent compared to the GPT-2 texts. Most notably, as the text length increases, the LSTM tends to deviate from the original context of the prompt while the GPT-2 stays closer to the topic.  
Figure 5: How well do the synthetic corpora reflect the real PHI distribution? This figure shows the differences to the PHI distribution of the language model training data (cf. Table 1).  
Figure 6: Text sample from the GPT-beam corpus (translated from Dutch, PHI highlighted and replaced with random identifiers). The structure of the generated text resembles a template that nurses used in the real data to report on dental hygiene of a patient.
## 2 Experimental Section
### Dynamic Biosorption Procedure  
#### 2.3.1 Dynamic Biosorption Working Methodology  
Dynamic biosorption studies were performed using a glass column with an inner diameter of 1.5 cm and a length of 21 cm. The column was packed with known varying amounts of residual biomass, immobilized in sodium alginate (solid biosorbent) in the form of granules with a diameter of 0.5 mm (around 3.8-6.77 g) providing a height/depth of fixed/packed bed of 3.8-7.0 cm. A dye solution with a known concentration (usually 76.72 mg/L) was inserted at the top of the column using a feeding funnel to ensure a uniform and continuous flowing regime. The passing of the dye solution through the column was done gravitationally, and the treated effluent was collected at the bottom of the column (outlet) for further analysis and control. At specific time intervals (10 min), 5 mL-samples of treated effluent were taken from the column outlet and analyzed with a UV-vis digital spectrophotometer to measure the residual dye concentration in the analyzed sample. In order to evaluate the proposed dynamic adsorption models, all experiments had to be performed considering the following operating parameters:  
1. The feeding temperature and pH were stabilized at 25 \({}^{\circ}\)C and 3.0 based on the best results found in the batch adsorption study (static regime) [27];
2. Three different flow rates (\(F_{vil}\) = 2.9, 4.5 and 7.3 mL/min) of dye-containing solutions with the same concentration were passed through the fixed/packed biosorbent bed in the column, corresponding to the mass of biosorbent (m) equal to 3.800, 4.074 and 6.770 g, respectively.
3. All experiments were stopped when saturation was achieved and after further control (at least three samplings after the saturation point had equal or higher values than the initial dye concentration in the collected treated samples).
and _Rhizopus_ spp., although known as phytopathogens that cause significant crop losses, also exhibit strong saprophytic capabilities, allowing them to colonize plant by-products. Under nutrient-limited conditions, certain species of _Colletotrichum_ are capable of increasing the production of reactive oxygen species (ROS), leading to abnormal fungal growth, while some _Rhizopus_ species can dehydrate colonized plant tissues and secrete a wide array of extracellular enzymes that facilitate growth on lignocellulosic substrates [14]. Although various microorganisms such as _Trichoderma_ spp. and _Aspergillus_ spp. have been extensively studied and widely applied due to their recognized degradative potential, there is limited evidence regarding the applicability of _Colletotrichum_ and _Rhizopus_ species as biodegraders of agricultural residues [13; 14]. Therefore, evaluating their potential could provide a foundation for expanding the range of biotechnological applications in organic waste degradation, addressing certain practical limitations observed with other microorganisms, such as the need for pretreated substrates or limited fiber decomposition. The concept of quality of an agricultural product is closely related to the presence or absence of foreign bodies that can cause contamination, as well as to the study of physicochemical parameters and leachate production that characterize it [15]. The physicochemical composition of a compost produced by the action of exogenous decomposer microorganisms is complex to predict, due to the inherent interaction of the genetic variability of the decomposer microorganisms and environmental factors during waste disintegration. Data mining techniques allow processing large volumes of information in two-dimensional planes and offer a more comprehensive visualization of the dynamics of the synthesizing microorganisms. There are several algorithms and methodologies that focus on the relationship between different attributes, such as the PCA biplot. The application of these algorithms to by-products made from organic waste promises to positively revolutionize waste management [16]. The main objective of this article was to use the multivariate PCA biplot technique to determine the dynamics of two types of phytopathogenic microorganisms in the decomposition of organic sugarcane waste through the analysis of the proximate composition of the compost obtained. In addition, the biodegradation characteristics, microbial incidence, and amino acid content were evaluated using the Duncan test (\(p<0.05\)) to assess the efficiency and effectiveness of the decomposers in the biodegradation process compared to the control treatment, where no new microorganisms were inoculated. The Duncan test was chosen for its ability to detect differences between treatments with greater sensitivity for balanced samples, making it suitable for biological studies with hierarchical responses. Unlike Tukey or Bonferroni, which are more conservative, Duncan allows a more flexible comparison of treatment effects without being excessively restrictive [17].
## 2 Analysis Methods
### Code Validation
#### 2.2.2 Comparison with the Experimental Results  
The amount of energy deposited in the flow can be obtained by Equation (1), however the ratio of the applied energy to the absorbed energy is unknown. In a previous study [PERSON] _et al._ (2018), it was assumed that the same energy is deposited when the simulation shows similar wave propagation and interaction phenomena to that in the experiment. Therefore, a reasonable absorption rate was sought by observing the flow characteristics according to the applied energy and comparing the result with the experiment.  
Figure 2 shows Schlieren images from the experiment and simulation. As mentioned, it is hard to determine how much energy is absorbed into the flow, so a parametric study was performed with changes in either the radius of the focus or the deposited energy. The structures of the flow were  
\begin{table}
\begin{tabular}{c c} \hline \hline Mach Number & 3.45 \\ Static temperature [K] & 77.8 \\ Static pressure [Pa] & \(1.31\times 10^{4}\) \\ Sphere Diameter [m] & \(2.54\times 10^{-2}\) \\ Deposition distance at the center line [m] & \(2.54\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flow conditions in the wind tunnel test (calculated).  
compared, including the bow shock, reflected shock, blast wave, and vorticity. To do so, numerical Schlieren images were directly superimposed on the experimental results. The images had a fixed aspect ratio and the scale was determined by comparison of the diameter of the sphere.  
The results in Figure 2 were obtained with a radius of about 1.2 mm and energy deposited at 36.78 mJ/pulse. In the experiment, the energy that was applied to the flow was 283 mJ/pulse, which means that the absorbed energy is about 12% of the applied energy. The results showed good agreement with the experimental results in terms of the formation of the blast wave, the propagation, and the interaction with the bow shock in front of the sphere. Furthermore, there was very good agreement between the results of the complex vortex structure due to the lens effect [24], which results from the interaction between the bow shock and a blast wave that was observed at 30 us after the deposition. In the dual solution problem, the same absorption rate was applied even if the deposition energy changed.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 3 GOCDM Underwater Acoustic Communication System  
GOCDM is an extended form of OCDM. We first present the modulation scheme of GOCDM, followed by a proof that it retains the same convolutional properties as OCDM and exhibits complete frequency band diversity for the subcarriers. We then introduce its application in transmitter and receiver systems for underwater acoustic communication.
# Design of a Compressor Test Rig for Immobilization of the Stall Cell
## 1 Introduction
While some of these rigs offer a suitable platform for experimental analysis of the flow field, most lack specific design considerations for rotating stall research, resulting in inadequate axial dimensions to encompass the entire range of stall cells. Moreover, due to the pronounced unsteadiness in the rotating stall flow field, a majority of experimental measurements heavily rely on high-frequency probes; however, their limited frequency response poses a constraint. Using the stall cell as a benchmark, its major unsteadiness is effectively eliminated, while the secondary unsteadiness remains weak, as previously mentioned. In this relative coordinate frame, the rotor and stator rotate in opposite directions, resembling a counter-rotating-type compressor. The measurement techniques suitable for flow analysis, such as oil flow visualization, tuft flow visualization, and pneumatic multi-hole probes, can be employed to acquire detailed insight into the flow structure within the stall cell. Keeping this in mind, a low-speed counter-rotating compressor test rig was designed and commissioned as an integral part of the present study. The primary objectives of this research encompass elucidating the employed design methodology for developing the compressor, determining crucial parameters such as pressure ratio, formulating a comprehensive design for both the compressor and test rig systems, initiating operational procedures to evaluate performance metrics, and conducting experimental investigations to characterize operational behavior under stall conditions. These findings will significantly contribute to future advancements by facilitating effective integration and utilization of circumferential distortion mesh technology for successful mitigation of stall cell formation while showcasing the preventive capabilities of this technology. This paper's contributions are twofold: firstly, it presents the design, characterization, and commissioning of a novel compressor test rig, which is now available for immobilizing stall cells; secondly, it develops a processing method to obtain two-dimensional velocity vectors and limit streamlines of the flow field from images measured using oil flow visualization measurement techniques.
## 3 Results and Discussions
### Influence of Environment Temperature and Time on the Piezoelectric Properties  
The goal of this subsection is to test the piezoelectric properties' stability of the cellular EVA. The first experiment consists of measuring the piezoelectric constant as a function of the environmental temperature. The purpose of this experiment is to show the effect of temperature on the material. The first phase in the experiment was to apply a gradual temperature rise while maintaining constant pressure (5 MPa). The temperature was gradually raised from 0 to 180 \({}^{\circ}\)C, with a 20 \({}^{\circ}\)C increment every 10 min. The second experiment is to measure the piezoelectric constant as a function of time. The objective of this experiment is to show the capacity of the material to store electrical charges. An experimental measurement of the d\({}_{33}\) piezoelectric coefficient as a function of time and temperature is shown in Figures 6 and 7. Figure 6 presents the changing of the piezoelectric constant with de-poling temperature for several silicone oil-ionized samples.  
Figure 5: Stress–strain hysteretic response of EVA 0% (Black curve), EVA 37% (Red curve) and EVA 65% (Blue curve) with various cycles. (**a**) 4 Hz and (**b**) 20 Hz.  
Figure 6: Piezoelectric coefficient as a function of depolarization temperatures charged in the silicone oil at room temperature.  
The principal experimental results demonstrate that when temperature rises, the piezoelectric coefficient remains stable until it reaches a critical limit, at which point the piezoelectric constant reduces. The pores consolidate beyond this point, and the film loses its cells, as well as the local dipoles that provide the pseudo piezoelectric effect. The phase transition solid-liquid temperature is observed when the piezoelectric constant begins to decrease. Thermal depolarization shows a remarkable difference between a sample which contains charges in the defaults without pores (EVA 0%) and a sample which contains ionic dipoles with porous structure (EVA 37% and EVA 65%).  
Figure 7 illustrates the temporal evolution of the piezoelectric coefficient, which was measured outside of the laboratory. After the relaxation period, the measurements were collected at room temperature. The d\({}_{33}\) piezoelectric coefficient value for EVA 65% reduced from 5.1 to 4.5 pC/N from 0 to 65 h, after which it stabilized at 4.5 pC/N. However, these piezoelectric activities were unstable, and a reduction was observed 65 h after the ionization step. Moreover, the charge in cellular EVA remained constant after this time.
## 2 Experimental Investigation
### Signal Analysis  
To analyze the signals acquired during the different tests, three different signal analysis methods were applied: coherence between the sensor and the PZT, \(FRF\) (frequency response function) between the sensor and the PZT and amplitude demodulation analysis to every sensor signal. These methods are explained in detail in the following sections.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 3 Numerical Simulation
### Analysis of Numerical Simulation Results
#### 3.4.2 Effect of Prestress Loss Rate on Horizontal Deformation of Formation  
After the completion of foundation pit excavation, the horizontal displacement cloud map of rock and soil mass at the middle section of the model was extracted, as shown in Figure 6. The horizontal displacement of rock and soil mass also mainly occurs within the  
Figure 4: Cloud map of vertical displacement of the rock mass after foundation pit excavation with different prestress loss rates (unit: m). (**a**) \(\beta=0\%\); (**b**) \(\beta=10\%\); (**c**) \(\beta=20\%\); (**d**) \(\beta=30\%\); (**e**) \(\beta=40\%\).  
Figure 5: Maximum settlement of the rock and soil mass under different prestress loss rates.  
depth of 10 m, and the maximum displacement gradually increases with the increase in the prestress loss rate.  
The maximum horizontal displacement of rock and soil mass under different excavation depths and prestress loss rates is shown in Figure 7. The horizontal displacement has a linear relationship with the time rate of the anchor cable prestress and increases gradually with the increase in excavation depth. When the excavation is completed, the maximum horizontal displacement of rock and soil mass (18.22 mm) when the prestress loss rate is 40% increases by about 10% compared with the condition without prestress loss (16.54 mm). The loss of prestress significantly reduces the constraint effect of the supporting structure on the rock and soil mass outside the pit.
## 2 Experimental Method
### Test Electrical Performance
#### 2.2.3 Measurement Uncertainty of DC Resistance  
Some factors that can affect testing reliability are cell temperature, calendar aging, etc. Hence, the definition of internal resistance and an accurate measurement of internal resistance is very important [27]. According to Ref. [28], the hybrid pulse power characterization (HPPC) test is used to measure the DC resistance, the calculation formula of which is as follows:  
\[\mathrm{R}=\frac{U_{1}-U_{2}}{I}, \tag{2}\]  
where \(U_{1}\) represents the no-load voltage and \(U_{2}\) represents the voltage of 100 ms measurement time after the voltage drop, and \(I\) represents the discharge [28].  
An accurate measurement of internal resistance is critical, thus Equation (2) was used in this paper for bias estimation. The first item is the method error, the second item and the third item are the instrument error, and the fourth item is the environment error. The SOC, state of health (SOH), current, temperature and time of measurement can affect the accuracy and consistency of the measurements [29]. In this paper, all internal resistances are measured in the 75% SOC, the discharge current is 1C, the ambient temperature is 25 \({}^{\circ}\)C \(\pm\) 3 \({}^{\circ}\)C, and the time of measurement is 100 ms; therefore, the influence of these factors on internal resistance will be greatly reduced. As for SOH, the cycle life of the test battery is greater than 1000; therefore, the effects of a few cycles on the internal resistance measurement are not very large. Therefore, in this paper, the internal resistance measurement is very accurate. The measurement uncertainty of resistance can be calculated by the following equation [28]:  
\[\Delta R=\bigg{|}2\frac{U_{1}-U_{2}}{I^{2}}\bigg{|}|\Delta I|+\bigg{|}\frac{1 }{I}\bigg{|}|\Delta U_{1}|+\bigg{|}\frac{1}{I}\bigg{|}|\Delta U_{2}|+|T_{c}| |\Delta T|, \tag{3}\]  
where the \(\Delta I\), \(\Delta U_{1}\) and \(\Delta U_{2}\) measurement uncertainty are caused by measuring instruments; the accuracy of the battery test system used in this test is 0.01% full scale (FS). The \(\Delta T\) measurement uncertainty are caused by the ambient temperature; the effect of this part can be neglected in this experiment as the ambient temperature remains unchanged. Consequently, the results of \(\Delta I\), \(\Delta U_{1}\) and \(\Delta U_{2}\) calculations are 0.6 mA, 1 mV and 1 mV, respectively, and the value of \(\Delta R\) is about 0.625 m\(\Omega\). Eventually, the DC resistance can be written in (\(R\)\(\pm\)\(\Delta R\)) format.
## 1 Introduction
### Our Results  
The contributions of this paper can be summarized as follows.  
For pattern matching with mismatches:  
* An algorithm for pattern matching with mismatches and wild cards that runs in \(O(n\sqrt{g\log m})\) time, where \(g\) is the number of non-wild card positions in the pattern; see Section 2.2.1.
* A randomized algorithm that approximates the Hamming distance for every alignment, when wild cards are present, within an \(\epsilon\) factor in time \(O(n\log^{2}m/\epsilon^{2})\) with high probability; see Section 2.3.  
For pattern matching with \(k\) mismatches:* An algorithm for pattern matching with \(k\) mismatches, without wild cards, that runs in \(O(n\sqrt{k\log k})\) time; this algorithm is simpler and has a better expected run time than the one in [11]; see Section 2.2.3.
* An algorithm that tests if the Hamming distance is less than \(k\) for a subset of the alignments, without wild cards, at a cost of \(O(k)\) time per alignment, using only \(O(m)\) additional memory; see Section 2.2.2.
* A Las Vegas algorithm for the \(k\) mismatches problem with wild cards that runs in time \(O(nk\log^{2}m+n\log^{2}m\log n+n\log m\log n\log\log n)\) with high probability; see Section 2.4.3.  
The rest of the paper is organized as follows. First, we introduce some notations and definitions. Then, we describe the exact, deterministic algorithms for pattern matching with mismatches and for \(k\) mismatches. Then, we present the randomized and approximate algorithms: first the algorithm for approximate counting of mismatches in the presence of wild cards, then the Las Vegas algorithm for \(k\) mismatches with wild cards. Finally, we present an empirical run time comparison of the deterministic algorithms and conclusions.