esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 4 Results
### DTMC-\"Content Viewing\" Screens
Thus, the matrix of the \"content viewing\" page (Figure 21) reveals that participants are likely not as interested in the music button since they opt to view other sections first and position the music button last. Apart from the matrices, Table 4 below shows the probability distribution over initial states and steady states for the DTMC model. Under the column of initial state probability, State R has the highest value, indicating that all participants are more likely to look at state R first when they reach this 'content viewing' screen in BT Player, which can also be interpreted as \"the background image attracts the most attention at first sight.\" The column of steady state probability reflects the likelihood that participants will view each block over time. State O, for example, has the highest value, indicating that the gaze is most likely to reach and terminate in that block (\"programme paskshots\"). However, state D has the lowest score, indicating that after participants have explored the page for some time, they are least likely to view the'sport button'. Additionally, the \"most likely trajectory\" of the DTMC is generated by Equation (13), \(S^{max}=\{\)R,..., T,..., O... \(\}\), which is calculated by the simulation of Markov transitions in R. The trajectory is depicted and mapped to the real-world screen layout as the most likely gaze path on the screen, which is shown in Figure 22. \begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **Initial Probability** & **Steady Probability** \\ \hline B & 0 & 0.013 \\ D & 0 & 0.001 \\ E & 0.143 & 0.018 \\ F & 0 & 0.010 \\ G & 0 & 0.006 \\ H & 0 & 0.002 \\ I & 0.071 & 0.005 \\ J & 0 & 0.008 \\ K & 0 & 0.009 \\ L & 0.143 & 0.032 \\ M & 0.143 & 0.075 \\ N & 0 & 0.047 \\ O & 0.071 & 0.303 \\ P & 0 & 0.059 \\ Q & 0 & 0.005 \\ R & 0.286 & 0.088 \\ S & 0 & 0.038 \\ T & 0.143 & 0.281 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial state and steady-state distribution–DTMC for “content viewing” screen. In addition, we have two implementations of the DTMC visualization in R and D3.js, as shown in Figures 23a and 23b, respectively. Intuitively, T is the state that most other states will jump to. Figure 23a is a static image, while Figure 23b is a screenshot of the animation of a Markov Model process on the browser. In Figure 23b, the thickness of arrows and directed lines indicates the value of probability. The animation of each transition is based on the transition matrix.
## 4 Results  
### Experimental Setup  
We employed the surrogate gradient method [29] to train our recurrent SNN model. In this work, we trained the models for 100 epochs, with a batch size of 16 on Nvidia V100. We performed the experiments on the modified audio dataset in [18]. Henceforth, we refer to the modified audio dataset as the Siren dataset. We obtained the results in terms of accuracy and onset latency. The LI cell outputted predictions based on the threshold value. Accuracy was based on total correct predictions vs total predictions at every time step. Onset latency was calculated as the time to obtain the detection of the siren onset event if a siren prediction was expected.  
First experiment setup: In order to evaluate the sliding windows with different sample sizes, we set constant parameters for LIF neurons in the hidden layer of the SNN. The threshold voltage of neuron was set to 1 V, both the time constants (membrane and synaptic) were set to 2 ms. We designed experiments with variation in window length (w = 2\({}^{x}\), i.e., 4096, 2048, \(\ldots\), 512) and hop lengths (h). We chose three setups for our evaluation, h = w (no overlap), h = 0.5 w (50% overlap) and h = 0.25 w (75% overlap) with three different mel channels.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 2 Related Work
### Task Offloading Strategy Design  
Considering the fine-grained energy utilization in order to prolong the useful life of satellites, an iterative algorithm is proposed in [20] to minimize the system weighted energy consumption while satisfying latency requirements in task offloading. This scheme saves a significant portion of system energy consumption compared with other offloading schemes. A greedy task allocation algorithm is proposed in [21] for SEC performing in the Walker satellite constellation, where each task is uniquely selected to reduce the average cost of task computing. A mechanism is designed in [22] that maximizes the network performance by jointly optimizing the computation offloading scheme, transmission scheduling discipline, and a pricing rule. A distributed computing offloading method based on game theory is proposed in [23]. This method enables mobile device users to improve the overall performance of the network through the trade-off between local computing and offloading to the edge server. It also involves non-cooperative game interaction with other users when offloading computations occur between multi-user mobile devices and edge servers.  
A multi-hop satellite peer offloading (MHSPO) algorithm is proposed in [24] for the time-varying satellite network topology. This algorithm solves the problem of how to achieve efficient offloading cooperation under the conditions of limited resources, thus further improving service quality and resource utilization.
# Improving Safety in Food Allergen-Specific Oral Immunotherapy
## 3 Food Allergen Immunotherapy
### Adjuants in Oral Immunotherapy  
ARs during OIT are not rare, and, in some patients, a favorable outcome is not so clear. Anaphylaxis pronounced gastrointestinal symptoms, eosinophil esophagitis, and other ARs are responsible for 10-30% of OIT withdrawals [48].  
A preferred outcome of OIT would be long-term allergen tolerance or SU, defined as a prolonged state of immune unresponsiveness that persists after allergen withdrawal [48]. Unfortunately, only one-third of the patients suffering from milk, egg, or peanut allergies achieve SU after OIT. Additionally, half of those patients will lose tolerance during the first six months following OIT [49]. On the other hand, a common outcome of OIT in food allergies is desensitization. In these cases, the threshold for allergic reaction is increased when regular allergen ingestion is maintained [50]. Patients are protected against the incidental ingestion of the culprit allergen, but only to a limited quantity. ARs will follow if the threshold amount is breached. Another negative side of desensitization is the vast number of patients experiencing ARs. There are data showing that three in four desensitized individuals experienced ARs to previously tolerated doses of OIT allergens [51; 52]. ARs resulted in the discontinuation of OIT in almost half of the patients. Additionally, desensitization can be lost over time [52]. One approach to ameliorate the mentioned limitations of OIT is the introduction of adjuvant therapy. By suppressing acute ARs and modulating the immune response, adjuvant therapy aims to reduce the frequency of ARs and promote tolerance in patients receiving OIT.
## 4 Experimental Verification
### Measurement of the Angular Indexing Error of the Spindle  
In a GMM, the angular position of the spindle is detected by an encoder at the bottom of the shaft. Due to the inherent 6-DOF geometric errors of the axis of rotation, the actual rotated angle of the spindle (also called the rotary stage) is different from the encoder reading [14]. Such a difference must be measured in advance for error compensation. As shown in Figure 9, a high precision multi-tooth indexing table (Jiujiang Instrument Company, Jiujiang, China, model ZDFT-720 teeth, calibrated accuracy \(\pm 0.1^{\prime\prime}\), uncertainty \(\pm 0.06^{\prime\prime}\)) was adopted as a reference angle device. Each time the spindle rotated to a prescribed angle, the reference indexing table rotated the same angle in the reverse direction. The reflector, mounted onto the indexing table, would associate with the autocollimator (Tianjin ATMV Company, Tianjin, China, model Automat 1000 UH-3050; calibrated accuracy: \(\pm 0.2^{\prime\prime}\); uncertainty: \(\pm 0.1^{\prime\prime}\) (\(k\) = 2)) to display the angular positioning error of the spindle. In the experiment, the spindle was rotated each time by a step size of \(15^{\circ}\) for one complete cycle. Figure 10 shows the calibrated indexing errors, called the angular positioning error, conforming to ISO230-7. The error would reset to zero when the spindle turned a full cycle. It can be seen that this spindle has a lagged phase, a maximum of \(-11^{\prime\prime}\) at \(\theta=170^{\circ}\), and then gradually catches up. This is the angular positioning error of the rotary stage itself.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 3 Stimuli-Responsive Polymeric Nanosystems  
Stimuli-responsive nanoparticles are degradable nanosystems engineered from specific polymeric materials that undergo functional or structural changes after being triggered by external stimuli. After being trigged by specific stimuli, the loaded (encapsulated or conjugated) drugs will be released by deshedding or breaking the linking bonds, thus realizing controlled drug release [100, 101]. The tumor hallmarks that can be exploited as stimuli such as tumor overexpressed enzymes, mild acidic pH, and redox potential are called endogenous stimuli and the triggers such as light, thermo, and ultrasound that are from outside of the body are termed exogenous stimuli. Both endogenous and exogenous stimuli can be introduced to polymeric nanosystems to develop stimuli-sensitive nanomicelles to minimize the side effects by controlling their drug release activities with spatiotemporal precision, hence enhancing the anti-tumor efficacy. In the following part, we will present the widely used stimuli-responsive polymeric nanosystems including endogenous platforms: enzyme-responsive, pH-responsive, redox-responsive, and exogenous ones: light-responsive, thermo-responsive, and ultrasound-responsive choices.
## 2 Materials and Methods  
Figure 2 illustrates the integrated analytical framework developed to evaluate the spatial alignment between the supply and demand of GSI in the GBA. The framework comprises three main components: I. Data Sourcing and Processing: This initial stage involves the compilation, georeferencing, and standardization of spatial indicators relevant to GSI supply and demand. Supply-side indicators include vegetation cover, waterway networks, and parks, while demand-side indicators encompass socio-demographic vulnerability, urban infrastructure, and flood exposure. All indicators are normalized using min-max scaling, with entropy weighting and Analytic Hierarchy Process (AHP) applied to assign integrated weights. II. Simulation of Supply-Demand Coupling: In this stage, composite indices for GSI supply and demand are calculated at the district level. The CD is used to measure the interaction strength between supply and demand subsystems, while the CCD assesses the overall alignment within broader urban socio-ecological contexts. Districts are classified into coordination categories based on CD and CCD thresholds, revealing degrees of imbalance. III. Proximity Matrix Construction and Network Clustering: To  
Figure 1: Geographic extent of the study area and the distribution of primary waterway networks across the Guangdong–Hong Kong–Macao Greater Bay Area.  
further interpret spatial disparities, a proximity matrix is generated based on the Revealed Comparative Advantage (RCA) of demand indicators. Using this matrix, a network of districts is formed through a maximum spanning tree algorithm and community detection (Fast-Newman method). The resulting clusters reflect similar demand profiles and serve as references for targeted, context-specific GSI planning.  
Figure 2: Framework for optimizing the spatial balance of Green Stormwater Infrastructure (GSI) in the Greater Bay Area (GBA), comprising three components: (I) data sourcing and processing; (II) simulation of supply–demand coupling; and (III) proximity matrix construction and network clustering.
## 2 Materials and Methods
### Membrane Parameters  
1. Ion exchange capacity: The dry mass of the membranes was measured on an analytical balance, and the sample was immersed in a 12 v% HCl solution for 48 h to protonate the sulfonic acid groups. The membrane samples and storage bottles were thoroughly washed with Milli-Q water before immersing the samples in a 1 M NaCl solution for 48 h. The protons in this NaCl solution were titrated with a 0.5 M NaOH solution using phenolphthalein as the indicator. The IEC was calculated using the following formula: \[\text{IEC (mmol/g)}=\frac{c_{NaOH}\cdot V_{NaOH}}{m}\] in which \(c_{NaOH}\), \(V_{NaOH}\) and \(m\) are the concentration of NaOH (mmol/L), the volume of NaOH used during the titration (l) and the mass of the tested membrane sample (g), respectively. 2. Water and methanol uptake: The membrane samples were dried for 20 h at 55 \({}^{\circ}\)C, and the dry membrane mass was determined. Afterward, the membranes were soaked in pure water/methanol for 24 h. The membranes were taken out of the corresponding bath, excess liquid droplets were removed from the surface using a paper towel, and the mass of the membrane samples was determined. The uptake was calculated as follows: \[\text{Water uptake (}\%\text{)}=\frac{m_{water}}{m_{dry}}\cdot 100-100\]  
Figure 2: Identification of the different carbon types in a sPEEK backbone. The identification of the carbon types is used to calculate the degree of sulfonation [31].  
\[\text{Methanol uptake}\ (\%)=\frac{m_{methanol}}{m_{dry}}\cdot 100-100\]  
in which \(m_{dry}\) is the weight of the dry membrane sample (g), and \(m_{water}\) and \(m_{methanol}\) are the membrane mass (g) after being soaked for 24 h in water and methanol, respectively.
3. The cation/anion permeation of the membranes was determined following the same procedure as reported by [PERSON] et al. and [PERSON] et al. [33; 34]. First, the membrane was immersed in a 0.1 M KCl solution for 24 h. To perform the experiment, the membrane (active area 19.63 cm\({}^{2}\)) was positioned in a two-compartment cell equipped with two saturated calomel electrodes. The donor compartment was filled with a 0.5 M KCl solution and the receiving cell with a 0.1 M KCl solution. The potential difference over the membrane was measured by means of the potentiostat Origaflex 01A from Origalys (France). The setup was left unattended until a stable value was reached (approximately 5 to 10 min). The cation/anion permeation was calculated, dividing the experimental potential difference (\(V_{exp}\)) by the theoretical potential difference (\(V_{ther}\)), as can be seen in the following equation:  
\[\alpha\ (\%)=\frac{\Delta V_{exp}}{\Delta V_{ther}}\cdot 100\]  
The same experiment was performed using a combination of 0.1 M NaHCO\({}_{3}\) and 0.5 M NaHCO\({}_{3}\). The theoretical potential difference was calculated with the Nernst equation using the appropriate activity coefficients and was equal to 36.47 mV and 36.13 mV for the experiment using KCl and NaHCO\({}_{3}\), respectively [35; 36].
## 3 Results
### Effects of the Environmental Humidity on Strength
#### 3.1.2 Flexural Strength  
The flexural strength of the cement paste was evaluated under different humidity conditions, and the results are shown in Figure 2. Under sealing and RH50% \(\pm\) 5% conditions, the flexural strength of the cement paste decreased with an increase in the further hydration time. In contrast, under the conditions of RH \(\geq\) 95% and water soaking at 20 \({}^{\circ}\)C, the flexural strength increased with further hydration time. This is consistent with the experimental results reported by [PERSON] [29].  
Under sealing and RH50% \(\pm\) 5% conditions, the flexural strength of cement paste after further hydration for 180 d decreased by 50.1% and 40.1%, respectively, compared to that before further hydration. Under the conditions of RH \(\geq\) 95% and water soaking at 20 \({}^{\circ}\)C, the flexural strength increased by 75.2% and 76.9%, respectively. Therefore, the evolution laws of flexural strength of cement paste under four different humidity conditions can be divided  
Figure 1: Determination curves of the compressive strengths under different humidity conditions.  
into two categories. The self-shrinkage of the cement paste under the sealing conditions and the self-shrinkage and dry shrinkage of the cement paste under the conditions of RH50% \(\pm\) 5% caused damages or cracks. Compared to the compressive strength, the flexural strength was more sensitive to damages or cracks in the cement paste, particularly surface cracking. Thus, the flexural strength decreased under the sealing and RH50% \(\pm\) 5% conditions. Notably, under the conditions of RH \(\geq\) 95% and water soaking at 20 \({}^{\circ}\)C, the supply of water vapour and water in the external environment accelerated the further hydration reaction of the cement paste, and the further hydration products continued to fill the internal pores of the cement paste, resulting in an increase in its flexural strength.
## 2 Literature Review
Encoder-based models, such as GBERT and GELECTRA, process input bidirectionally, allowing them to capture syntactic relationships more effectively [12]. These models have the potential to perform well when fine-tuned on domain-specific datasets. However, their reliance on labeled training data limits their adaptability to new text domains, such as OCR-corrected literature. In contrast, decoder-based models, such as GPT-4o and Llama, operate in an autoregressive manner, generating text one token at a time based on prior context [15]. These models have gained popularity due to their zero-shot and few-shot learning capabilities, enabling them to generalize across different languages and text structures without extensive fine-tuning [7; 8; 9; 16]. Recent studies have also explored whether improved prompt engineering can enhance the grammatical correction capabilities of decoder-based models, such as GPT-3.5. [PERSON] et al. [17] found that carefully structured prompts, incorporating explicit correction instructions and contextual constraints, significantly improved the ability of GPT-3.5 to maintain semantic integrity while reducing unnecessary corrections. Despite their advantages, decoder-based models may exhibit overcorrection tendencies, where they modify text unnecessarily, altering its original meaning [13]. In scanned documents of children's literature--especially where illustrations are omitted--the sentences are often quite short and may contain very few words. If language models like GPT-3.5 or others attempt to enhance these sentences by adding extra words, they risk altering the simplicity and clarity that are essential for young readers. This could lead to the loss of the original meaning, making the text more complex or introducing unintended interpretations. While significant progress has been made in GEC, several research gaps remain:  
* Limited research on GEC for OCR-generated German text, particularly in the domain of digitized literature. * A lack of comparative studies on encoder-based vs. decoder-based models for German GEC, with most prior work focusing on English. * An insufficient analysis of overcorrection tendencies in decoder-based models, which may impact text accuracy in real-world applications like digitized children's literature. This study aims to address these gaps by systematically evaluating different GEC models for OCR-generated German text and assessing their strengths and weaknesses in handling grammatical errors in children's literature.
# Research on the High Speed of Piston Pumps Based on Rapid Erecting of Launch Vehicles
## 4 Results and Discussion
### Mechanisms for Achieving High Speed of Piston Pumps
The following equation is defined:  
\[\alpha=\frac{\gamma}{\beta} \tag{20}\]  
where \(\alpha\) is the effective transport rate characterising the intensity of the backflow, \(\beta\) is the filling rate when the piston chamber is at the upper dead centre and \(\gamma\) is the volumetric efficiency. The larger the value of \(\alpha\), the weaker the backflow, and the smaller the value of \(\alpha\), the stronger the backflow. With the improved cylinder block, the effective transport rate increased by 11.064%, 23.208% and 22.401% for the three groups of conditions, respectively. The effective transport rate of the piston chamber is significantly increased, and the backflow is reduced based on the improved cylinder block. Backflow takes place when the piston chamber is moved from the suction to the discharge area. Figure 14 depicts a cloud of backflow in the piston chamber of a traditional block when the rotational speed is 2000 rpm, ranging from 209\({}^{\circ}\) to 212\({}^{\circ}\). The velocity vector is represented by the vector arrows, while the pressure is represented by the other cloud. Backflow will impact the piston chamber wall as the cylinder rotates, as shown in region C of the diagram. Figure 15 depicts the flow rate in the piston chamber's discharge area. Backflow is shown by a positive flow rate in the graph, while normal discharge is represented by a negative flow rate. The maximum flow rate of backflow is reduced after the cylinder is improved, the duration is greatly shortened and the backflow phenomenon is reduced. The overall backflow mass of the traditional and improved blocks is 0.00374 kg and 0.00253 kg, respectively. The total backflow mass of the improved block is reduced by 32.4%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rotational Speed**} & \multicolumn{3}{c}{**Traditional Cylinder Block**} & \multicolumn{3}{c}{**Improved Cylinder Block**} \\ \cline{2-7}  & \multirow{2}{*}{**Filling Rate**} & **Volume** & **Effective** & \multirow{2}{*}{**Filling Rate**} & **Volume** & **Effective** \\  & & **Efficiency** & **Transportation Rate** & & **Efficiency** & **Transportation Rate** \\ \hline
2000 & 89.703\% & 74.465\% & 83.013\% & 97.532\% & 91.755\% & 94.077\% \\
2500 & 81.055\% & 49.649\% & 61.254\% & 91.089\% & 76.936\% & 84.462\% \\
3000 & 75.148\% & 34.390\% & 45.763\% & 82.753\% & 56.408\% & 68.164\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Filling rate and effective transportation rate of the piston chamber. Figure 14: Backflow cloud of traditional block with different angles at 2000 rpm. (**a**) 209\({}^{\circ}\); (**b**) 210\({}^{\circ}\); (**c**) 211\({}^{\circ}\); (**d**) 212\({}^{\circ}\). In summary, the improved cylinder block can effectively inhibit the cavitation and backflow phenomenon in the piston chamber, thus enhancing the volumetric efficiency of the piston pump, reducing the flow pulsation and realising the high speed of the high-pressure axial piston pump.