esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan
## 4 Data-Driven Analysis
### The Contribution of HSR to Land Prices  
The regression results above show that the HSR, as a representative variable for accessibility, displays a positive relationship on the land price, given presence of other contributable variables relating to economics, infrastructural and social factors. There is reason to believe that the impact of HSR on land prices may vary when underlying conditions governing these controlling factors change. In this sub-section, a principal component analysis (PCA) is carried out to observe associations among the variables used in Equation (2). Table 4 shows the loadings of the variables on the principal components in three-, four-, and five-dimensional spaces. When observing the three-dimensional principal components model outcome, it was found that HSR is associated with _GenPop_, _NumGuests_, _FTZ_, _AirDist_, and _CC_ in the first component. The size of general population and the number of guests provide justifications for the setting up an HSR station in a city. A longer distance away from the airport also means the HSR is necessary to provide connectivity between the city and the rest of the country, especially in support of the required transportation for functioning of the free trade zone. The connection between the Taoyuan HSR station to Taoyuan International Airport is a representative example. There is also a negative relationship between the size of the general population and the distance to an airport, simply because residents are less willing to stay nearby to airports (as hypothesized in the casual loop diagram in Section 3) and an airport needs a large space to operate. The positive correlation with the construction cost is likely to reflect the constrained supply of housing when constructions cost and demands for housing in a densely populated city are concurrently higher. The second component saw a positive association between _CPI_ and internet and a negative correlation with Forex. The degree of internet penetration is generally associated with the level of urbanization of a city which, in turn, correlates with higher prices. On the other hand, a weak exchange rate regime dampens demands and lower prices. The third component comprises the _UnEmp_ and _IRrf_, where the two variables relate negatively to each other. When the PCA reduces the dataset into 5 components, _HSR_, _GenPop_, and _CC_ are positively correlated with one another in component 1, while _NumGuests_, _FTZ_, and internet also positively correlated with one another in component 3. As before, _CPI_ negatively correlated with Forex in component 2 and _UnEmp_ and _IRrf_ continue to relate negatively to each other in the component 4. _AirDist_ becomes a standalone exogenous factor. Approximately 65% and 80% of the variance are explained using 3 and 5 principal components, respectively (Figure 8).
## 2 Improved Deep Learning Model for Day-Ahead Solar Irradiance Forecasting
### Long Short Term Memory Based Sequence Forecasting Model (from RNN to LSTM)
#### 2.3.1 Recurrent Neural Network  
The traditional neural network structure is characterized by the full connections between neighboring layers, which can only map from current input to target vectors. However, RNN has the ability to map target vectors from the whole history of the previous inputs. Thus RNN is more effective at modeling dynamics in sequential data when compared to traditional neural networks. In general, RNN builds connections between units from a directed cycle and memorizes the previous inputs via its internal state. Specifically speaking, the output of RNN at time step t\(-\)1 could influence the output of RNN at time step t. This makes RNN able to establish the temporal correlations between present sequence and previous sequences. The structure of RNN is shown in Figure 5.  
Figure 4: The picture shows the framework of the CNN based local feature extractor. The convolution layer consists of different filters marked by yellow, green and grey colors. Each filter can generate a specific feature map to extract the key information of the raw sequence input through sliding the corresponding windows. The activation function is used to enhance the ability of models to learn more complex functions. The function of pooling is equal to subsampling as it subsamples the output of convolutional layer based on the definite pooling size.  
In Figure 5, the sequential vectors \(\mathbf{X}=[\mathbf{x}(0),\mathbf{x}(1),\mathbf{x}(2)]\) are passed into RNN one by one according to the set time step. This is obviously different from the traditional feed-forward network in which all the sequential vectors are fed into the model at one time. The relevant mathematical equation can be described as follows.  
\[\mathbf{S}(t)=\sigma(\mathbf{U}\mathbf{\cdot}\mathbf{x}(t)+\mathbf{W}\mathbf{\cdot}\mathbf{S}(t-1)+\mathbf{b}) \tag{10}\]  
\[\mathbf{y}(t)=\sigma(\mathbf{V}\mathbf{\cdot}\mathbf{s}(t)+\mathbf{c}) \tag{11}\]  
in which \(\mathbf{x}(t)\) is the input variable at \(t\) time step, \(\mathbf{W}\), \(\mathbf{U}\) and \(\mathbf{V}\) are weight matrixes, \(\mathbf{b}\) and \(\mathbf{c}\) are the biases vectors, \(\sigma\) is activation functions, and \(\mathbf{y}(t)\) is the expected output at \(t\) time step.  
Although RNN is very effective at modeling dynamics in sequential data, it can suffer from the gradient vanishing and explosion problem in its backpropagation based model training when modeling long sequences [53]. Considering the inherent disadvantages of typical RNN, its improved variant named LSTM is adopted in our work, which is illustrated in the following subsection.
## 5 Conclusions and Discussions
### Ether Group Has Significant Effects on Engine Combustion and Emissions  
Fatty acid has an enough long chain, but FAME's cetane number is not so high for its unsaturated bonds. By introducing ether group, the CN of new model biodiesel increased to be 70, a big improvement compared to FAME of 50, in a simple way. Engine combustion analysis indicates the following factors: higher cetane number shortens the ignition delay time, which increases the temperature of air fuel mixture within the scope of spray; shorter ignition delay wakens premixing combustion and results in lower NOx formation and emission; higher oxygen content decreases PM formation and emission; and both of higher CN and oxygen content together improve the biodiesel fuel combustion for the special chemical groups of ether and ester.  
The developed new model biodiesel have higher potential to decrease smoke, NOx, HC and CO than FAME. Most results indicated that FAME reduced smoke 30% to 50%, but the new model biodiesel reduced engine smoke as high as 80%. It also has significant reducing effects on NOx, HC and CO. Concerning that the test engine was a type of Euro 1, further studies should be carried out with engines meeting euro 6 to make sure its potentials.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 3 Model Lightweighting Based on Global Dynamic Channel Pruning  
### Dynamic Model Pruning  
In order to better apply the coal and gangue detection algorithm to hardware devices, considerations must be given to the detection speed, computational load, and model weight. Therefore, this paper employs the global channel pruning algorithm to lightweight the detection model. The Performance-Aware Global Channel Pruning (PAGCP) [22], based on performance-aware approximation for multitask models, achieves model compression by pruning globally redundant filters. During the pruning process, the establishment of a channel saliency indicator model analyzes the correlation and interaction of different channel-layer convolution kernels, measuring the importance of filters within and between layers. In the network architecture, taking the \(i\)-th layer and the \(j\)-th convolutional kernel as an example, the saliency index is expressed by Equation (13).  
\[S\big{(}x;\theta_{ij}\big{)}=\big{\|}f\big{(}x;\theta_{ij}\big{)}-f\big{(}x; \theta_{ij}=0\big{)}\big{\|}_{r} \tag{13}\]  
where \(\theta_{ij}\) is a binary mask, and the convolutional kernel is deleted only when \(\theta_{ij}=0\); \(r\) is the order of the norm. The \(x\) represents the input of the saliency indicator and calculates the Euclidean distance in the convolutional kernel's Euclidean space. A larger Euclidean distance indicates greater importance for that convolutional kernel.  
Currently, global channel pruning primarily adopts a static pruning order, as illustrated in Figure 4a. The outcomes of static pruning depend on the chosen pruning strategy, introducing increased challenges to model optimization. This paper employs dynamic pruning by calculating the floating-point operations per second (FLOPs) for each layer of the model: First, the FLOPs contributions are dynamically sorted, and layers with significant FLOPs contributions but minimal impact on model performance are selected for pruning, retaining layers with smaller FLOPs contributions but significant improvements in model performance. Then, we determined the pruning ratios for all layers and reevaluated the contribution of each layer's pruning ratio to FLOPs, continuously adjusting the pruning order dynamically, as illustrated in Figure 4b. Finally, after pruning, the model's computational load and weight were reduced. While maintaining detection accuracy, these enhancements resulted in an improved inference speed and reduced deployment challenges on hardware devices.
## 5 Discussion  
The aim of this section is to establish the promising technologies and to address the technological challenges, to ensure they satisfy the measurement requirements for the observation gaps detected in the Copernicus space segment. As shown in Table 3, the measurements with gaps detected in this study can be monitored by different types of sensors. According to the state of the art of the potential payloads to cover the gaps of the Copernicus space infrastructure in 2020-2030, the next generation of instruments require overcoming challenges and new technological developments in order to meet the end-user requirements.  
GNSS-R is a promising technology to detect surface currents, significant wave height, sea ice cover, horizontal wind speed, dominant wave direction and sea ice thickness. The advantage of this technology is that it can process data in real time and onboard, through the use of Delay-Doppler Mapping (DDM); in this way, the latency time can be improved. However, the next generation of GNSS-R sensors should improve the spatial resolution to <10 km and the accuracy to <1 cm and <0.5 m/s, in order to meet the end-user requirements. In this regard, a precise clock module is required to reduce the errors in the retrieval computation.  
For the passive microwave, a spatial resolution of <25 km is feasible by increasing the size of the antenna and would be suitable on CubeSats, as well as meet the spatial resolution requirement for atmospheric pressure over the sea surface. Microwave radiometers on small platforms demand the use of inflatable antennas in order to improve the spatial resolution and meet the end-user requirements, covering a variety of measurements between sea ice parameters, ocean conditions and sea surface temperature.  
Future cloud radar, radar altimeters, LiDAR and SAR are required to improve the coverage and adopt the use of a new concept to allow a wide-swath. For the the next generation of scatterometer, it is imperative to improve the accuracy for horizontal wind speed over the sea surface <0.5 m/s with a spatial resolution <10 km, in order to meet the user requirements.  
Optical sensors (such multispectral, hyperspectral radiometers and spectrometers) can monitor many variables with gaps, but the data can only be acquired in daylight and clear sky, a limiting factor for observing polar regions, where it is very often cloudy and the dark period is long. However, they are a good complement to microwave sensors, where the fusion of data could result in better products in terms of spatial resolution and accuracy.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.9 Decomposition  
Decomposition allows us to have autonomous services organized around business capability services. it is necessary to separate the system into suitable pieces functionally and obtaining high cohesive and loosely coupled services are expected as a result of decomposition. The challenging point encountered first after deciding to go with MSA is to determine the right size of business capability and if it cannot succeed properly, MSA will not be an advantage and it might cause many problems in terms of mainly scalability, performance, availability and reliability.
## 2 End-of-Life of CRC
### Separation of CRC  
The following separation process is based on a German study [3] and was the result of experiments centered around separating CRC. CRC is selectively crushed with a carrier with a concrete pulverizer and sorting grab, resulting in pre-crushed coarse fragments of carbon concrete with carbon reinforcement. The fragments are loaded onto a dump truck with a hydraulic excavator and transported to a stationary preparation plant. The main crushing is performed by a jaw crusher, resulting in a heterogeneous mixture of building materials. The degree of disintegrating the carbon roving fragments from the concrete matrix is over 99% [3].  
In the pre-separation stage, metallic-embedded parts are removed from the material flow by a stationary magnetic separator. Concrete fines (largest grain size of 2 mm), light plastic components (e.g., spacers), and carbon reinforcement with a mass fraction estimated at 10% are removed using a cross-flow classifier. After the pre-separation, the material stream contains a heterogeneous accumulation of concrete fragments of grain group 3/56 and exposed carbon roving fragments (average length 80 mm) [3].  
A camera-based sorting unit is used for the main separation of fractions. According to [PERSON], 97.7% of the carbon roving fragments can be separated with this type of single-grain sorting. The result is the concrete fraction of grain group 3/56 (the utilization of which is not further discussed in this paper--mainly being used in road construction as gravel or partly in recycled concrete [18]) and the separated carbon fiber fraction including foreign mineral constituents of grain group 0/2 [3].
# Potential Marker Genes for Predicting Adipogenic Differentiation of Mesenchymal Stromal Cells
## Materials and Methods
### Adipogenic Differentiation of MSCs  
After four passages, MSCs were seeded at \(3\times 10^{3}\) cells per cm\({}^{2}\) in 24-well plates and maintained to 90% confluence in Dulbecco's modified Eagle's medium (DMEM; Sigma) supplemented with 10% fetal bovine serum (FBS, Hyclone), 100 U/mL penicillin G (Sigma), and 100 mg/mL streptomycin (Sigma). For adipogenic induction, the MSCs were maintained in DMEM (high glucose) (Sigma) containing 10% FBS, 10 mg/mL insulin (Wako), 0.2 mM indomethacin (Wako), 0.5 mM 3-isobutyl-1-methyl-xanthine (Wako), and 1 mM dexamethasone (Sigma) (adipogenic induction medium) for three days, followed by a 4-day incubation with DMEM containing 10% FBS and 10 mg/mL insulin (maintenance medium). The adipogenic treatment was repeated four times. GPDH activities were determined at 28 days using a GPDH activity assay kit (Hokudo), as previously described [10]. GPDH activities were normalized using genomic DNA content, which was determined by the PicoGreen fluorescence assay (Invitrogen).
## 4 Assessment of the Developed Approach
### Assessment of Structural Element Reconstruction
#### 4.3.3 Analysis of Points-to-Model Distances  
A second quality index taken into account is the signed distances between points composing an element and the reconstructed element and more specifically the statistical distribution of those distances. Figure 17 proposes two examples of point clouds of respectively a portion of facade wall (Figure 17a) and an indoor wall (Figure 17b). These point clouds are colourized according to the signed distances between points and the reconstructed element. The distributions of the signed distances are shown in the associated histograms representing the number of points related to the signed distances.  
Figure 16: Visualisation of standard deviations associated to walls (colour-bar unit in metres).  
Reconstructed walls or portions of walls are based on a set of points which contains inevitably artefacts or objects. It is for example the case, in Figure 17a, where window awnings and a painting emerge from the wall. It is essential that for points which are effectively on the wall, the distances points-to-wall are close to 0. Figure 17b presents an example of a wall very well reconstructed since the highest occurrence frequencies concern distances around 0 and the most part of distances are between \(-5\) mm and \(5\) mm. For point cloud of Figure 17a, the reconstruction is less satisfying because the most frequent signed distances are located around \(-5\) mm. This is due to the fact that the surface was reconstructed based on two planes located in two different rooms. For the plane located on the right, one can observed that distances are positives for one side and negatives for the other side. This means that the two planes are not utterly coplanar.
## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.3 Deep Learning Approaches  
Deep learning is a subset of machine learning that relies on neural networks as a fundamental framework. It has gained prominence due to the convergence of two factors: the availability of Big Data and the accessibility of cost-effective parallel computing hardware like Graphical Processing Units (GPUs) and computer clusters. Deep learning uses computational intelligence to acquire knowledge, learn from experience, and develop complex concepts from simple ones. Unlike traditional machine learning, deep learning can autonomously learn features from input data. A deep neural network develops its classification capabilities by analyzing thousands of labeled images during the training process. When presented with an input image or video, the neural network layers respond to complex shapes and structures, comparing them to the training data to identify the image or extract relevant features. The commonly used models are:  
* _Convolutional Neural Networks (CNNs)_. CNNs are pivotal in edge analytics, excelling in object detection and recognition. Their capacity to handle substantial quantities of visual data and discern complicated patterns is of utmost importance in the interpretation of meaningful observations from visual information.
* _Recurrent Neural Networks (RNNs)_. RNNs serve a pivotal function in video analysis by effectively managing tasks such as tracking, segmentation, and action recognition. With their proficiency in processing sequential data, RNNs are well suited for analyzing videos, which are essentially temporal image sequences.  
Deep learning excels at recognizing and classifying complex objects. Deep learning techniques, especially neural networks, have shown remarkable performance in early disease detection [66; 67; 71]. CNNs and RNNs have been used to analyze medical images and time series data for early disease detection [48].
## 3 Materials, Methods, and Findings
### LSTM Confusion Matrix  
The Long Short-Term Memory (LSTM) algorithm is used for time series and sequential datasets. The model is configured with the parameter's units = 100, dropout = 0.2, and recurrent_dropout = 0.2 for the input layer. These parameters enable the model to learn long-term dependencies and reduce the risk of overlearning. In the training process, the loss function 'binary_crossentropy' was used for binary classification. Figure 8 shows the complexity matrix of the LSTM algorithm.  
The model correctly classified 97 instances in the \"no verbal violence\" class and incorrectly assigned 5 instances to the \"verbal violence present\" class (false positive). In the \"verbal violence present\" class, the model made 91 correct predictions and misclassified only 17 instances as \"no verbal violence\" (false negative). These results show that the model has a high sensitivity (84.26% recall) in the \"verbal violence present\" class and keeps the false negative rate low. However, the presence of false positives (4.90%) indicates that the model can generate false alarms in the \"no verbal violence\" class.
## 8 Conclusions  
This paper introduced the main types and sources of remotely sensed data that are freely available and have cryospheric applications, including aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. Extensive information about these sensors, their cryospheric and polar applications and download sources/timeframes of data availability were provided in text, figures and tables. Derived data products are increasingly available, and examples of such products of particular value in polar and cryospheric research were presented. Information about available free and, where possible, open-source, software tools for reading and processing remotely sensed data were also included.  
Into the future, funding for upcoming sensors will continue to be contentious in both the public and private spheres. Support for missions will respond to demonstrated value from sensors, although it will be crucial to push for development missions, too. As we move forward, it is crucial that the community not only utilizes the openly available data, but also continues to tell people about it. Public engagement of many varieties to share beautiful images and compelling success stories is crucial to demonstrate the utility of having free and open access polar remote sensing data; open access publications by the community will forward this cause, too. Looking ahead, the community (researchers together with data curators and cyberinfrastructure partnerships) will be required to collect, manage and use an increasingly wide variety of polar and cryospheric remote sensing data, the majority of which will likely be trending to even more free and open access. Learning our lessons about the requisite skills, infrastructure, tools and communication of polar Earth observation now will ensure that even better data get into the hands of the researchers that need it; even the ones that do not know it yet.