esa-sceva/satcom-chunk-collection huggingface dataset:
# PyFlies: A Domain-Specific Language for Designing Experiments in Psychology
## 3 PyFlies Architecture
### PyFlies Design Principles  
The design of the PyFlies language was guided by the following principles:  
1. **Readability**: Researchers share experiments with their peers. Furthermore, it is a common belief that developers spend more time reading the code than writing it. That means the experiment descriptions should be as easy to read and understand as possible. One of the principles of Python Zen is \"readability counts\", which is arguably one of the design principles that had the most impact on its adoption.
2. **Abstraction**: PyFlies tries to hide as much of the implementation details as possible. Thus, in PyFiles experiment descriptions, most often, we use abstract terms. For example, instead of using explicit screen coordinates, we often use terms left, right, up, down, and let the compiler complete the mapping. These mappings, if needed, can be overridden. In different contexts, these abstract terms may mean different things. For example, a symbol left might be a position on the screen, a direction of the pointing arrow image stimulus, or a left arrow key on the keyboard. The code generator is capable of carrying out this context-aware mapping while the experimenter may still use the same term in all contexts. Another abstraction used in PyFlies is abstracting how exactly to carry out various steps in the experiment. The experimenter is more focused on providing information on _what_ needs to be achieved than _how_ to do it.
3. **Domain concepts**: PyFlies language constructs are domain-specific. For example, instead of having concepts of general-purpose programming languages such as _functions_, _classes_, _methods_, etc., in PyFiles we use _tests_, _screens_, _components_, _tinings_, _condition tables_, etc. In our experience, this reduces mental effort in designing and understanding the experiment.
4. **Modular architecture and extensibility**: PyFiles follows a modular approach where target platform code generators can be developed as separate Python projects (Section 3). Moreover, components used in the experiment description are specified using a component description DSL, which will make it possible to open this language to experimenters and enable custom component specification in the future (Section 4.4).
5. **Best practice**: The code generator should be written by developers that are knowledgeable about both PyFiles and the target platform. Thus, the current best practices (at least to the best of generator authors' knowledge) for the target platform are automatically provided for all PyFiles users. Since generators can be improved constantly, all PyFiles experiments undergo improvements for free.
6. **Focus on the experiment structure**: PyFiles specifications are strongly focused on the experiment structure. Each experiment consists of _screens_ and _tests_ which are executed during the _flow_ of the experiment. That is something often found in GUI builders but lacking in textual languages. Experiment [6], implemented as a Python library, is a notable text-based solution that employs this idea.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 3 Multi-Objective Integrated Optimization Model
### Objectives  
1 Minimize additional operation cost  
The additional operation mileage at the level of the railway network consists of the running mileage of the standby train sets for subsequent trains in circulation and the mileage back to the allocated station after the completion of the circulation:  
\[\text{min}{\sum}_{c\in C^{aff}}\sum_{b\in B}{\sum}_{t\in T}h_{b}^{c,t}{\times}( WM_{b}^{c,t}+ADD_{b}^{c,t})\times OC_{b} \tag{1}\]  
\(C\) is the set of train sets circulations in the railway network, train set circulation \(c\in C\). \(C^{aff}\) is the set of affected train set circulations. \(B\) is the set of standby train sets, standby train set \(b\in B\). \(T\) is the set of trains in the railway network, train \(t\in T\). \(T^{aff}\) is the set of affected trains. Binary variables \(h_{b}^{c,t}\) indicate whether the standby train set \(b\) executes train \(t\) of circulation \(c\) (\(h_{b}=1\)) or not (\(h_{b}=0\)). \(WM_{b}^{c,t}\) is the mileage of standby train set \(b\) after it completes the circulation \(c\) that contains train \(t\). \(ADD_{b}^{c,t}\) is the distance between standby train set \(b\) and its allocated depot after the standby train set \(b\) completes circulation \(c\) which contains train \(t\). \(OC_{b}\) is the operating cost per mileage of standby train set \(b\). 2 Minimize total delay  
The total delay includes three parts: the delay time of affected passengers who are served by other affected trains; the delay time of passengers who are served by standby train sets; and the delay time of passengers who cannot be served due to train cancellations:
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 5 Conclusions  
This study proposes a health monitoring system specifically developed for snoring detection using Tiny Machine Learning (TinyML) models. The primary objective of the research is to develop a TinyML-based snoring detection model to accurately identify specific audio patterns associated with snoring during sleep. The research is also aimed at designing TinyML models for resource-constrained Internet of Things (IoT) devices. To test the efficacy of the proposed model, the experiments were conducted in real-world sleep environments by deploying the TinyML model to resource-constrained wearable IoT devices. The results show that the model achieved high accuracy while using minimal computational resources on the target wearable device. The quantized model achieved an accuracy of 95.85% with a low latency of 48 milliseconds, RAM 17.0 K, and Flash  
Figure 8: Model performance on the test dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unoptimized Model (FLOAT32)**} & \multicolumn{2}{c}{**Quantized Model (INT8)**} \\ \hline  & **Classifier** & **Total** & **Classifier** & **Total** \\ \hline
**Latency** & 955 ms & 955 ms & 48 ms & 48 ms \\
**RAM** & 58.6 K & 58.6 K & 17.0 K & 17.0 K \\
**Flash** & 32.8 K & & 34.7 K & \\
**Accuracy** & **96.63\%** & & **95.85\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model comparison.  
34.7 K, making the model an ideal choice for wearable devices. This research demonstrates the feasibility and practicality of implementing snoring detection TinyML models on resource-constrained IoT devices and provides a non-intrusive method of sleep monitoring. The proposed research makes an important contribution to the advancement of TinyML applications in health monitoring.  
[PERSON] designed the methodology and built the TinyML model. [PERSON] conceptualized the idea for this manuscript, supervised and administered the work, prepared the original draft, and validated the data and results. [PERSON] designed hardware, curated data, and validated the data and results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the first author.  
The TinyML model and the graphs for this study were generated using the Edgeimpulse [16] platform.  
The authors declare no conflicts of interest.
## 4 Discussion
### The Mechanism of Pore Structure Differences
#### 4.1.2 The Mineral Composition and Fabric  
Except for the control effect of organic matter on pore structure [43], recently investigators have proved the positive effect of quartz and the negative effect of clay minerals on pore structure [22; 39]. However, previous research also point out that the clay mineral pores could occupy 22.7-75.5% (average 48.4%) of the total pores in the shale pore system and the positive correlation of clay mineral on macropore volume [27]. And by comparing shale from two different depositional environments, it can be discussed whether mineral composition and fabric have extensive control over pore structure in different lithologies. As Figure 8c presents, there is a positive relationship between macropore volume and clay mineral, and the correlation gradually decreases in the mesopore (Figure 8b). Furthermore, there is no obvious relationship between micropore volume and clay mineral (Figure 8a). Therefore, the mineral composition is the control factor in the macropore structure, while the organic matter shows more influence on the micropore and mesopore structure. Also, the inorganic pore of clay mineral is the main reason for the difference in macropore volume of marine shale and continental shale  
The specific surface area can reflect the connectivity and complexity of the pores [47]. However, unlike the strong positive relationship between the macropore volume and clay content, there is no significant correlation between the macropore specific surface area and the clay content (Figure 8c). The main reason is that the micropores and mesopores are the main contributors to the specific surface area, and the macropores contribute little to the total specific surface area. For example, macropores account for 23% of the total pore volume in continental shale but only 3% of the specific surface area, which greatly reduces the correlation between the macropore specific surface area and clay content. Another possible reason relates to the ductility of clay minerals, and most clay minerals are distributed in plates. These pores are prone to be compressed under high overburden pressure [41], which also leads to the reduction of the specific surface area.  
Figure 8: The correlation between the clay content and the pore volume (**a–c**) and the specific surface area (**d–f**). Part of data from ([PERSON] et al., 2020) [20].
## 4 Discussion
### Related Works

Table 4: Bandwidth measurement while the experiment. flow rules for a certain duration, and the idle timeout is used to remove unused flow rules. Because the purposes of hard and idle timeout are different, it is not efficient to use only one type of timeout. Therefore, we propose a new scheme that adjusts both hard and idle timeouts dynamically and simultaneously. Many researchers have brought ML/DL techniques to address DoS attacks in SDN [18; 19] or P4 switch [20] recently. Although ML/DL techniques are very powerful and effective, they require heavy computational power and a good dataset to properly train the model as well. According to [21], the ML/DL for intrusion detection/prevention system still has several problems to solve. For instance, [22; 23] showed either not enough performance or remarkable accuracy with a high false positive ratio. Since ML/DL-based techniques in the research area tend to take eviction mechanisms against the attack flow rules, a high false positive ratio is fatal. On the other hand, our proposed scheme is very lightweight and easy to scale due to its simplicity. The P4 switch has brought evolution in various fields of SDN. Moving some control logic back to the data plane again could solve the single point of failure problems and mitigate DoS attacks. Recently, researchers have shown interest in applying ML techniques to deal with slow-rate flow table overflow attacks [18; 23]. In particular, the authors of [18] showed that their proposed scheme can mitigate the attack with high real-time performance and timely response. However, their result shows that the scheme has higher flow table utilization than normal even though the proportion of the attack flows is decreased. This is because of the proportional eviction mechanism based on the detection result and it harms the efficiency of the network. Meanwhile, our scheme keeps 85% of the normal flow rules compared to the environment with hard timeouts and shows a low ratio of the attack flow rules. This means that it can maximize the flow table utilization by making the attack flow rules expire much faster than the normal rules while it effectively mitigates the flow table overflow attack as well. Table 5 summarizes the research domain, solution, techniques, and timeout usage of the related works.
## 2 Materials and Methods
### Data Collection
#### 2.2.1 Satellite Data
Radiometric calibration standardized the data across the acquisition dates, converting digital numbers into surface reflectance values. Atmospheric corrections were applied to account for the effects of aerosols and water vapor, improving the accuracy of the spectral bands used for the vegetation indices. Figure 2: Spectral reflectance curves of avocado orchards derived from Landsat 8 and 9 satellite data. The figure shows the distinct spectral bands (blue, green, red, near-infrared, and shortwave infrared) used to calculate the vegetation and water indices. The variation in reflectance values across these bands provides insights into plant health, water contents, and stress conditions. Seasonal changes in reflectance highlight the impact of varying water availability on vegetation indices, illustrating how water stress influences plant vitality throughout the growing season. For the analysis, the vegetation indices were computed for each satellite image, and seasonal trends were analyzed through temporal aggregation methods, including calculating seasonal averages and identifying peaks in the indices. This approach, used in several studies [37; 38; 39; 40; 41; 42], helped assess changes in vegetation health and water stress throughout the growing seasons. Additionally, soil moisture estimates from ERA5, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Data Store, were integrated into the analysis. ERA5 provides hourly soil moisture data with global coverage, providing a consistent perspective on soil water dynamics. These estimates were aggregated to match the temporal resolution of the satellite imagery, enabling the validation and contextualization of the remotely sensed soil moisture data.
## 2 Materials and Methods
### Outcome Measurements  
The data were collected during chart review, and included patient demographics (age and sex), pre- and postoperative clinical symptoms (paresthesia and diplopia), and time of operation. Hertel exophthalmometry was used to measure the enophthalmos. Pre- and postoperative orbital volumes were assessed using Mimics and 3-matic software (Materialise). The anterior reference plane was established, and three landmark points were identified on this plane: the supraorbital notch, zygomaticofrontal suture, and the inferior end of the anterior lacrimal crest margin (Figure 3). These landmark points served as crucial reference points for precise measurements of the orbital volumes before and after the surgical intervention. The main outcome measures were the pre- and postoperative orbit volumes of the injured eye, the orbital volume of the contralateral unaffected eye (normal eye), and the orbital volume discrepancy (VD), which was obtain by subtracting the volume of the contralateral unaffected eye from that of the injured eye.  
Figure 3: 3D volumetric measurements of the orbital tissue within the bony orbit. Preoperative assessment revealed the presence of herniated orbital tissue through the bony defect of the left orbital floor fracture, as indicated by the arrow (**A**). Following the surgical intervention, a postoperative assessment was performed, and it was observed that the volume of the herniated orbital tissue had markedly reduced (**B**).  
Figure 2: Technique for orbital implant preparation. The upper and lower portions of the templates were designed based on the virtual 3D orbital imaging of the fracture site (**A**). Unnecessary parts of porous polyethylene material containing an embedded titanium implant were carefully cut out (**B**). To create the customized orbital implant, the implant was positioned between the upper and lower portions of the templates, which were subsequently pressed together (**C**). This process ensured the precise formation of a customized 3D contour on the implant (**D**).
# Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
###### Abstract  
Residual stress is the main cause of flatness defects in sheet metal and the basic method to improve the shape quality of the sheet is to reduce and eliminate the residual stress by multi-roll levelling. The curvature coupling between repeated sheet bendings in multi-roll levelling greatly affects the accuracy of the analysis of the residual stress evolution, which is rarely considered in current research. Aiming to address this problem, a method for eliminating residual stress by multi-roll levelling based on curvature coupling is discussed in this article. An evaluation criterion and an analysis model are proposed to investigate the evolution of the residual stress in multi-roll levelling considering the curvature coupling between bendings. The effects of the intermesh of the work rolls and the plastic deformation of the sheet on the residual stress are also discussed. The results show that multi-roll levelling will cause rolling residual stress while reducing the initial residual stress of the sheet and the larger plastic deformation caused by the intermesh of the work rolls at the entry is beneficial for the complete elimination of the initial residual stress, but the rolling residual stress will increase at the same time. Therefore, the total residual stress of the sheet after levelling depends on the appropriate levelling parameters.  
Keywords:curvature coupling; residual stress; multi-roll levelling; sheet metal; work roll +
Footnote †: journal: Applied Physics Letters
## 3 Research on an Improved CGAN Ground Grid Imaging Algorithm
### Improved CGAN Structure
#### 3.1.2 Discriminator Design  
The discriminator uses the PatchGAN structure, which can effectively capture the high-frequency features at the patch (local area) level in the image. Compared with the global discriminator, PatchGAN divides the input image into several small blocks and judges the authenticity of these small blocks one by one to ensure that the model can learn the local texture and high-frequency information in the image [23]. Its expression is Equation (11).  
\[D(I)=\sum_{i}L(D(\hat{x}_{i}),y_{i}) \tag{11}\]  
Figure 7: Attention module diagram.  
\(\hat{x}_{i}\) is the \(i\) th patch in the image \(I\). \(D(\hat{x}_{i})\) is the judgment result of the discriminator on the \(i\) th patch, outputting a scalar value indicating the probability that the patch is a real image, and \(y_{i}\) is the label of each patch (1 for real images and 0 for generated images).  
The discriminator D uses the convolutional network as its core structure. First, it constructs a fully connected layer and a deformation layer to convert the \(13\times 16\) voltage sequence into a feature map of \(64\times 64\), which is combined with the generated image or the real image into a multi-channel feature map and then input into a four-level convolutional network structure. Each layer of the convolutional network consists of convolution, pooling and activation layers. The convolution kernel size is \(5\times 5\) and the step size is 2, which gradually reduces the spatial dimension of the feature map to extract high-level feature information. The four-level convolutional layers contain 64, 64, 128 and 256 filters, respectively, and a batch normalization layer is used to normalize small batches of data to accelerate the training process and improve the stability of the model. After structured analysis, the data are flattened by the flattening layer, connected to two fully connected layers for further decision-making and mapped by the Sigmoid activation function in the output layer to judge the authenticity of the image. The Sigmoid function is Equation (12):  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{12}\]
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 3 Methodology
### Decoder  
The tag decoder or an output layer is the last stage in an NER model that takes the input of context-dependent representations and outputs a tag sequence corresponding to the input. The CRF is an effective approach for sequence tagging problems [50] because it learns the scoring function from tag pairs, such as [B-I-O] in the training phase [24]. It is useful to consider the correlation in the neighboring labels [8] and model the tag sequences jointly with CRF [51] instead of modeling them independently. For example, in NER with a standard (B-I-O) tagging scheme [52], I-GPE can not follow I-ORG. Therefore, the modeling of independent assumptions would be impossible. Formally, given a sentence \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) with a predicted label sequence \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\), the \(tanh\) function is used firstly to predict the confidence score for each possible label:  
\[o_{i}=\tanh\bigl{(}W_{c}h_{i}^{\prime}+b_{c}\bigr{)} \tag{7}\]  
where \(W_{c}\) and \(b_{c}\) are the trainable parameters.  
The CRF incorporates the transition information between succeeding NE tag sequences to obtain optimal tags over the entire sentence. In this way, CRF jointly decodes the best chain of NE tags instead of decoding each label individually. For a given sentence \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), the definition of the probability for the prediction result is given as follows:  
\[s(X,y)=\sum_{i=1}^{n}\bigl{(}o_{i,y_{i}}+T_{y_{i-1},y_{i}}\bigr{)} \tag{8}\]  
where \(y\in\) (B-I-O) labels; the scoring function \(s(X,i)_{y_{i}}\) is the output of the hidden layer at \(i\)th token; \(T\) denotes the scores of any two adjacent labels, and \(T_{y_{i-1}}\) represents the score from the successful transfer of the label \(y_{i-1}\) to the label \(y_{i}\); \(o_{i,y_{i}}\) represents the confidence score of the \(y_{i}\)-th label of each token.  
The decoding is the search for a sequence of NE tags \(y\) with the highest conditional probability. We compute the probability \(p\) of the label sequence \(\hat{y}\) and output the label sequence \(\overline{y}\) by using the Viterbi algorithm [53]. The loss function is defined as:  
\[p(\hat{y}\mid\mathbf{X})=\frac{e^{s(x,\hat{y})}}{\sum_{\hat{y}\in Y_{x}}e^{s( x,\overline{y})}} \tag{9}\]  
\[\text{loss}=-\sum_{i=1}^{N}\log p\left(\hat{y}^{(i)}\mid x^{(i)}\right) \tag{10}\]  
\[\overline{y}=\arg\max_{y\in Y_{x}}s(X,y) \tag{11}\]  
We use the gradient backpropagation method to minimize the loss function.