esa-sceva/satcom-chunk-collection huggingface dataset:
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 3 Enhanced PCP Feature  
### Normalized Logarithmic PCP Feature  
Our system begins by extracting suitable feature vectors from the raw audio. Like most chord recognition systems, we use a chromagram or a PCP vector as the feature vector. [PERSON] and [PERSON] propose a 12-dimensional feature vector-quantized PCP [8; 29] that determines the proper frequency resolution and is sufficient for separating musical notes by low-frequency components.  
The calculation of PCP feature vectors can be divided into the following steps: (1) using the constant \(Q\) transform to calculate the 36-bin chromagram; (2) mapping the spectral chromagram to a particular semitone; (3) median filtering; (4) segmenting the audio signal with a beat-tracking algorithm; (5) reducing the 36-bin chromagram to a 12-bin chromagram based on beat-synchronous  
Figure 1: Chord estimation system.  
segmentation; (6) normalizing the 12-bin chromagram. The reader is referred to [15] for more detailed PCP calculation steps.  
In the beat-synchronous (tactus) segmentation, we use the beat-tracking algorithm proposed by [PERSON] [44]. This method has proven successful for a wide variety of signals. Using beat-synchronous segments has the added advantage that the resulting representation is a function of the beat, or tactus, rather than time.  
Unlike most of the traditional PCP methods, we determine the normalized value using the \(p\)-norm and logarithm. The formula is as follows:  
\[QPCP_{\log}\left(p\right)=\log_{10}[C\cdot QPCP_{12}\left(p\right)+1] \tag{1}\]  
\[QPCP_{norm}\left(p\right)=QPCR_{\log}\left(p\right)/\left|\right|QPCP_{\log} \left|\right| \tag{2}\]  
After applying the logarithm and normalization, the chromagram is called the LPCP.  
The left image of Figure 2 shows a PCP of the **C** major triad and the right image shows its LPCP. The strongest peaks are found at C, E, and G because the **C** major triad comprises three notes at C (root), E (third), and G (fifth). Figure 2 demonstrates that LPCP more clearly approximates the underlying fundamental frequencies than PCP.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 7 Discussion
### The Role of Examples (Few-Shot Learning) (H2.1 and H2.2)  
Our findings partially support H2.1, indicating that few-shot prompting generally improves performance, particularly in precision and resource mapping accuracy (Tables 3 and 5). This can be attributed to several factors. Firstly, providing an example helps the LLMs to understand the desired output format and structure more clearly, reducing ambiguity and guiding the models to generate more accurate FHIR bundles. Secondly, examples serve as a form of contextual learning, allowing the LLMs to better interpret the nuances of the clinical data and map them correctly to the corresponding FHIR elements. Lastly, examples may help to mitigate hallucinations by anchoring the models' responses to a concrete reference, thereby reducing the likelihood of generating irrelevant or incorrect information. However, we also confirmed H2.2, identifying the risk of overreliance on examples, where the models reproduced placeholder values instead of extracting information from the input reports. This highlights the delicate balance between providing sufficient guidance and avoiding overfitting. Interestingly, the failure rate was reduced with few-shot prompting at higher temperatures compared to lower temperatures. This could be due to the fact that higher temperatures allow the models to deviate more from the examples, thus mitigating the over-reliance issue. This finding underscores the importance of incorporating examples in prompts when using LLMs for structured data conversion tasks in healthcare, particularly when dealing with sophisticated and standardized formats like FHIR.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 5 Experimental Evaluation
### Information Retrieval from Privacy Policies
#### 5.1.1 Privacy Policy Dataset  
In order to experiment our techniques, we used a large annotated corpus of privacy policies. The dataset, called APP-350, was developed within the MAPS project [10]. The dataset consists of a set of 350 privacy policy text files. All files were subdivided into paragraphs, each one annotated by law students. Every annotation consists of a tag, applied to paragraphs, which indicates that the paragraph contains a privacy policy description. For example, the _Contact_ tag was attached to those paragraphs referring to contact information practice. A paragraph can also be tagged with different annotations at same time: e.g., the same paragraph may describe both demographic and contact privacy practices. In that corpus, five generic tags (i.e., contact; location; demographic; SSO = single sign on; identifier)were used, plus more specific sub-tags (e.g., ContactEmailAddress; ContactPhoneNumber...). In total, there were 30 different tags that could be applied to the paragraphs, which are listed in Table 1.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 2 The KMALL Data Format
### SKM Datagrams  
Sensor Attitude Data (SKM) is another common type of datagram from the KMALL format. It may contain several sensor measurements, and it is usually present in.kmall and.kmwcd files. Its structure is much simpler than that of MRZ or MWC, as can be observed in Figure 5.  
As usual, the first structure is a header to identify the datagram type and size. The second one contains information about the _SensorSamples_ such as the number of samples \(N_{S}\) or the number of bytes per sample \(N_{B}\). Finally, the last structure contains samples retrieved by the sensors such as attitude data. Most of the fields are floating point numbers; hence, we need to pay special attention here when performing the pre-processing.
## 4 Experiments
### Evaluation Indicators  
In order to quantitatively assess the effectiveness of the method in this paper, the evaluation indexes in the experiments are Accuracy (Acc), Completion (Comp), and Overall, the units are millimeters (mm). Acc calculates the average distance from the point cloud reconstructed by the model to the corresponding points of the real point cloud; Comp calculates the average distance from the real point cloud to the point cloud reconstructed by the model; and Overall calculates the average value of Acc and Comp, which reflects the overall quality of the reconstruction results.
## 3 Implementation  
### 3D Gauss Map  
The 3D Gauss map is a 3D variant of the current 1D Gauss form developed and used for the first time for cryptography applications. The 3D Gauss map equations are originally derived from (5) and expressed as:  
\[x_{i+1}=e^{(-cz_{i}^{2})}+d+by_{i}^{2}x_{i}+az_{i}^{3} \tag{7}\]  
\[y_{i+1}=e^{(-cz_{i}^{2})}+d+bz_{i}^{2}y_{i}+ax_{i}^{3} \tag{8}\]  
\[z_{i+1}=e^{(-cz_{i}^{2})}+d+bx_{i}^{2}z_{i}+ay_{i}^{3} \tag{9}\]  
Here, \(x\), \(y\), and \(z\) are used as intervals, whereas the real numbers \(a\), \(b\), \(c\), and \(d\) are employed as inputs. It is a very secure system using quadric, cubic, and quadratic coupling in the equations, as well as four constant components. During this process, the 3D Gauss developer generates a 3D Gauss map. Using the initial conditions of \(x_{1}=0.4250\), \(y_{1}=0.5250\), \(z_{1}=0.6250\), \(a=0.0235\), \(b=0.0377\), \(c=4.9\), and \(d=-0.68\), the resulting sequences are chaotic.  
Several processes are done on the image in this technique for encryption. Row permutation operations are conducted in the first phase, followed by row rotation operations. When a random sequence is sorted by the key \(x\), the pixel order in each row is permuted. The intermediate phase rotates pixels in rows based on whether the random sequence is odd or even. The operations of column permutation and column rotation are accomplished in the following phases. A pixel column permutation in which pixels are shifted about in accordance with a random sequence \(y\). At this stage, the pixels in the column are rotated dependent on whether the random sequence is odd or even. In the last phase, the key sequence created by the key \(z\) is applied column by column to the whole image's bits, which have been partitioned into 8-bit blocks or shuffled images. Figure 6 explains the process of the 3D Gauss system that includes key generation and permutation combination.  
Figure 6: 3D Gauss map system.
## 3 Data and Methods
### Available Geotechnical Data and Assessment of the Post-Liquefaction Consolidation Subsidence  
We calculated the post-liquefaction consolidation subsidence thanks to an extensive database of field and laboratory investigations collected by [PERSON] et al. [60] over the study area. The database comprises over 1800 geotechnical, geophysical, and hydrogeological investigations from several studies (Figure S2a) (see [PERSON] et al. [60] and references herein). In particular, we exploited the available cone penetration tests (CPT, CPT, CPTU in [PERSON] et al. [60]) to provide a first-order estimation of the post-liquefaction consolidation subsidence in the study area.  
Volume changes because of the dissipation of earthquake-induced excess pore water pressures cause the post-cyclic consolidation of loose soil deposits. In free-field conditions, if the stratigraphy can be assumed to be horizontally layered, it is reasonable to assume that little or no lateral displacements occur after the earthquake, such that the volumetric strain will be equal or close to the vertical strain. Therefore, in the following, it will be assumed that, in free-field conditions, the dissipation of excess pore pressure induces a post-cyclic consolidation subsidence that can be computed as follows:  
\[w=\sum_{i}^{n}\frac{\Delta u_{i}}{E_{ood,i}}\Delta z_{i}\, \tag{3}\]  
where \(\Delta u_{i}\) is the excess pore water pressure generated by the seismic event in the \(i\)th layer of the \(n\) layers in which the soil profile is divided. \(E_{ood,i}\) is the constrained or oedometric modulus of the generic layer \(i\), and \(\Delta z_{i}\) is its thickness.  
The required parameters in Equation (3) for each saturated soil deposit were estimated based on simplified considerations and approaches. We first defined the soil behavior type from the available cone penetration test (CPT, CPTU, and CPTE) according to the non-normalized Soil Behavior Type Index (\(I_{SBT}\)) defined by [PERSON] [61] as:  
\[I_{SBT}=\Big{[}(3.47-\log(\
icefrac{{q_{c}}}{{p_{a}}}))^{2}+(\log(\
icefrac{{ f_{i}}}{{q_{c}}}\cdot 100))^{2}\Big{]}^{0.5}, \tag{4}\]  
where \(q_{c}\) and \(f_{s}\) are the cone tip resistance and sleeve friction, respectively, while \(p_{a}\) is the atmospheric pressure.  
We then classified as sand-like soils those with \(I_{SBT}<2.6\), while clay-like soils as those with \(I_{SBT}>2.6\).
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 2 Piezoelectric Materials
### Perovskite
#### 2.2.1 Lead Perovskite Materials  
After realizing the low performance of ZnO-based PENGs, researchers diverted their focus to lead zirconate titanate [Pb[Zr(x)Ti(1\(-\)X)O\({}_{3}\)] (PZT)-based ceramic materials. PZT ceramics exhibit good crystallinity, a high dielectric constant, and a high piezoelectric constant. These superior properties have attracted the attention of many researchers, thus resulting in tremendous developments in relation to PZT-based PENGs. PZT is highly piezoelectric; however, fabricating a thin film is laborious owing to its britteness. The complex synthesis process and their brittle nature are major obstacles in the fabrication of PZT-based PENGs. Although researchers have made numerous attempts to overcome these obstacles by fabricating different morphologies and applying unique processes, the developments in PZT-based PENGs has dropped exponentially due to the hazardous nature of lead. Consequently, attention is now being paid to exploring lead-free perovskite materials in PENGs [25; 26].
## 3 System Architecture for Hand Therapy
### IoT and Complexity of the Proposed Architectures  
The data from the wearable device is sent to the cloud using Message Queuing Telemetry Transport (MQTT) protocol. The MQTT protocol and the COAP is used to address the targeted needs of IoT applications [37]. MQTT is much suited for the multiple architectures proposed for our wearable device compared with that of COAP. COAP uses one-to-one protocol whereas MQTT uses many-to-many protocol for the exchange of data between clients and servers. MQTT is a publish-subscribe message protocol that works on top of Transmission Control Protocol (TCP)/Internet Protocol (IP). MQTT needs a very low bandwidth for communication, which helps in controlling the wearable even when the internet speed is slow.  
The complexity of the proposed architectures varies depending on the single user or multiuser system. As real time feedback is expected in the therapist therapy and user therapy architectures, the complexity of these systems should be as low as possible. In the case of battery-operated wearables, the power consumption and energy requirements of the system should be optimal. This can be achieved by avoiding computationally complex operations by measuring only the required parameters.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 6 One-Class SVM Classifier
### One-Class SVM Training  
After transforming the \(N\) training traces into a set of feature vector \(\mathbf{x}_{1},...,\mathbf{x}_{N}\), we can train the one-class SVM for normal activities. The basic idea is to find a sphere that contains most of the normal data such that the corresponding radius \(R\) can be minimized:  
\[\min_{R,\xi,\mathbf{a}} R^{2}+\
u\sum_{i=1}^{n}\xi_{i}\] \[s.t. ||\mathbf{x}_{i}-\mathbf{a}||^{2}\leq R^{2}+\xi_{i}\] \[\xi_{i}\geq 0\]  
The slack variables \(\xi_{i}\) are introduced to allow some data points to lie outside the sphere, and the parameter \(\
u\geq 0\) controls the tradeoff between the volume of the sphere and the number of errors. Using the dual representation of the Lagrangian [34], the objection function is equivalent to  
\[\min_{\alpha}\sum_{i,j=1}^{n}\alpha_{i}\alpha_{j}\langle\mathbf{ x}_{i}\cdot\mathbf{x}_{j}\rangle- \quad\sum_{i=1}^{n}\alpha_{i}\langle\mathbf{x}_{i}\cdot\mathbf{x}_{i}\rangle\] \[s.t. 0\leq\alpha_{i}\leq\
u,\sum_{i=1}^{n}\alpha_{i}=1 \tag{15}\]  
This quadratic programming (QP) problem can be solved using standard optimization techniques [35]. To determine if a testing sample is within the sphere, the distance to the center of the sphere has to be calculated. If the distance is larger than the radius \(R\), the testing sample is considered abnormal.  
Typically, the training samples are not spherically distributed in the input space. Thus, the original data points are first mapped into a feature space so that a better data description can be obtained. Instead of requiring an explicit mapping function from the input space to the feature space, the solution can be obtained by replacing all the inner products \(\langle\cdot,\cdot\rangle\) in Equation (15) by a kernel function \(k(\cdot,\cdot)\):  
\[\min_{\alpha} k(\mathbf{x}_{i}\cdot\mathbf{x}_{j})-\sum_{i=1}^{n}\alpha_{i}k( \mathbf{x}_{i},\mathbf{x}_{i}) \tag{16}\]  
In our context, due to the noisy and nonlinear characteristic of the PIR sensors, the decision boundary of the OSVM is quite complex. Thus, we apply the Gaussian Radial Basis Function (RBF) kernel for the OSVM, which is defined as follows:  
\[k(\mathbf{x}_{i},\mathbf{x}_{i})=exp(-\gamma||\mathbf{x}_{i}-\mathbf{x}_{j}|| ^{2}) \tag{17}\]  
where \(\gamma>0\) is a scaling factor that controls the width of the kernel function.
###### Abstract
However, most of these studies use near infrared (NIR) spectroscopy to perform classification research [10; 11], and just a few works focus on the content of methanol in the gasoline [12]. Thus, more explorations should be researched, and the feasibility of IR spectroscopy should be attempted to qualitatively and quantitatively measure the methanol gasoline. Due to IR spectroscopy having the special information of molecular profiles, attributes correspond certain ones to functional groups of molecule. Thus, the feasibility of IR spectroscopy for rapidly detecting methanol in methanol gasoline was investigated. In this study, firstly, the dataset was explored using unsupervised principal component analysis (PCA), then qualitative and quantitative supervised models, including partial least squares discriminant analysis (PLS-DA), partial least square regression (PLSR) and least squared support vector machine (LS-SVM), were carried out to detect the methanol percentage in methanol gasoline. Concerned with the problem of too many variables in the stage of building the regression models, two classic variables selection algorithms (i.e., uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)) were also applied to select the optimal variables. Finally, the performance of all regression models were systemically compared to identify the best prediction model for methanol percentage in methanol gasoline. To assess the feasibility of IR spectroscopy for detecting the methanol percentage in methanol gasoline, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy coupled with several multivariate calibration models, and variables selection methods for qualitative and quantitative analyzing methanol were used to investigate. Based on above introduction and planning, it can be seen that the main objective is to qualitatively and quantitatively detect methanol using the IR spectra. To be specific, there are several sub-subjects: (1) collecting and analyzing the spectral data of gasoline and methanol gasoline; (2) qualitatively classifying the gasoline and methanol gasoline; (3) selecting the optimal variables for regression model; (4) establishing the quantitatively detection model for methanol based on full variables and the optimal variables; (5) identifying the best detection model by comparing all model's performance.
###### Abstract  
This paper proposed an entropy weight optimum seeking method (EWOSM) based on the typical scenarios partitioning and load distribution matching, to solve the reactive power optimization problem in distribution network under the background of big data. Firstly, the mathematic model of reactive power optimization is provided to analyze the relationship between the data source and the optimization schemes in distribution network, which illustrate the feasibility of using large amount of historical data to solve reactive power optimization. Then, the typical scenarios partitioning method and load distribution matching method are presented, which can select out some loads that have the same or similar distributions with the load to be optimized from historical database rapidly, and the corresponding historical optimization schemes are used as the alternatives. As the reactive power optimization is a multi-objective problem, the multi-attribute decision making method based on entropy weight method is used to select out the optimal scheme from the alternatives. The objective weights of evaluation indexes are determined by entropy weight method, and then the multi-attribute decision making problem is transformed to a single attribute decision making problem. Finally, the proposed method is tested on several systems with different scales and compared with existing methods to prove the validity and superiority.  
entropy weight optimum seeking method (EWOSM); big data; reactive power optimization in distribution network; typical scenarios partitioning; load distribution matching; multi-attribute decision making 2017
_Article_  
Carbon Dioxide Uptake by MOC-Based Materials  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 1,* 2020**  
Received: 6 March 2020; Accepted: 24 March 2020; Published: 26 March 2020  
**The highest potential of magnesium oxychloride cement (MOC) is its capability to be used as a component of low-energy building composite materials while acting as a CO\({}_{2}\) sink. The results of this contribution also show that MOC can be used as a binder in advanced building materials that have particular properties, and therefore specific application potentials. Formation and hardening of this material are rather fast, so the material can be used in quick repairs as well as a protection layer. This property is also beneficial for use in prefabrication, due to the possibility of unmolding after a shorter time compared to Portland cement (PC) materials, so the whole production process can be considered more effective. Somewhat significant importance should be given to its ability to capture CO\({}_{2}\), which not only makes it more eco-friendly, but also improves its mechanical properties.**  
In this work, carbon dioxide uptake by magnesium oxychloride cement (MOC) based materials is described. Both thermodynamically stable magnesium oxychloride phases with stoichiometry 3 Mg(OH)\({}_{2}\).MgCl\({}_{2}\).8H\({}_{2}\)O (Phase 3) and 5 Mg(OH)\({}_{2}\).MgCl\({}_{2}\).8H\({}_{2}\)O (Phase 5) were prepared. X-ray diffraction (XRD) measurements were performed to confirm the purity of the studied phases after 7, 50, 100, 150, 200, and 250 days. Due to carbonation, chlorartinite was formed on the surface of the examined samples. The Rietveld analysis was performed to calculate the phase composition and evaluate the kinetics of carbonation. The SEM micrographs of the sample surfaces were compared with those of the bulk to prove XRD results. Both MOC phases exhibited fast mineral carbonation and high maximum theoretical values of CO\({}_{2}\) uptake capacity. The materials based on MOC cement can thus find use in applications where a higher concentration of CO\({}_{2}\) in the environment is expected (e.g., in flooring systems and wall panels), where they can partially mitigate the harmful effects of CO\({}_{2}\) on indoor air quality and contribute to the sustainability of the construction industry by means of reducing the carbon footprints of alternative building materials and reducing CO\({}_{2}\) concentrations in the environment overall.**  
**magnesium oxychloride cement; MOC phases; carbonation; CO\({}_{2}\) uptake; carbon footprint**