esa-sceva/satcom-chunk-collection huggingface dataset:
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
## 3 Concept of the Selection Set
These rules for partial model extraction can be categorized into three types: basic rules for a valid IFC file, extraction rules based on Selection Set, and processing rule for redundant information, as shown in Figure 1.  
Basic rules for a valid IFC file: The basic rules refer to the fundamental requirements that should be complied with when an IFC file is formed. The proposed method is to export an IFC file, so the extracted partial model should also comply with these basic rules. Extraction rules based on Selection Set: The extraction requirements were included in the Selection Set. In order to query and extract the matching IFC data, the mapping from elements in the Selection Set to IFC model data was developed. Processing rule for redundant information: Required data could be identified according to the basic rules and extraction rules. In the final step to export the IFC file, the information that is not required by business tasks should be filtered to ensure the validity of the IFC file. Traditionally, the existing extraction methods generally process BIM data based on one IFC version. When the IFC version is updated, it is clear that defining a mapping between one version and the updated one is a major undertaking. It may be needed to modify the corresponding algorithm. Through the proposed rules, the data processing of partial model extraction was divided into different steps. It can significantly reduce the modification work of the proposed algorithm because of the  
Figure 1: Rules for partial model extraction. updated IFC version, ensuring the stability of this method. The data processing flow can be concluded as follows. The first step is to query corresponding IFC data according to the user-defined requirements. These requirements (such as object types, properties, and relations) can be obtained from the Selection Set. In Step 2, the target object will be located through the IFC reference relationship, and all of its corresponding attributes will be remained. Finally, all the target objects and their attributes will be extracted to form a new IFC file. The following subsection describes the proposed rules in detail.
## 6 Data Transmission Using XOR Encoding and Decoding
### Encoding Process  
When the hub shares encoded packet bundles with another hub, it collects an array of coefficients, namely \(\text{encv}=(\text{encv}_{1},\text{encv}_{2},\ldots,\text{encv}_{n})\), termed as the encoding vector, which is derived from \(\text{ENV}(2^{s})\). While a hub collects the array of the \(k\) bundles, \(E_{m}\) (\(m=1,2,3,4,\ldots,k\)) is encoded straight away into a structure of a solitary yield packet bundle, which is derived as  
\[\text{EPB}=\sum_{m=1}^{k}\text{encv}_{m}\cdot E_{m},\text{where encv}_{m}\in\text{ENV}(2^{s}) \tag{27}\]  
The currently encoded packet bundle is forwarded with 'k' coefficients in the model. The receiving hub utilizes a homogeneous encoding vector so as to decode the packet bundle.
## 4 Results  
### Geological Mapping  
The geological map of the Manzai Ranges was prepared using Sentinel-2 data and previously published maps [27; 28]. The band ratios enhanced lithological contacts, competent and incompetent rock units, and different generations of alluvial deposits and deflected streams; however, some individual rock units/formations remained undivided (Figure 7). Quaternary sedimentary rocks are the dominant rock types mapped in these ranges. The two mapped thrusts are the Khirgi and Jandola ones, as displayed. The Jandola Thrust emplaces the older Litra Formation over the younger Chaudhwan Formation while the Khirgi Thrust emplaced the older Chinji and Nagri Formation (undivided) over the younger Chaudhwan Formation in the foreland. Three different generations of stream mouth deposits/alluvial fans were identified. The regional slope direction is southeast; however, the active streams are deflected to the west at the fault contact. Major streams are actively entrenched into the old alluvial surfaces. Additionally, the Manzai anticline, composed of the quaternary Chaudhwan Formation, shows an active fold growth into the recent alluvial deposits.  
Figure 7: Geological map of the Manzai Ranges prepared using the Sentinel-2B dataset and older map. Quaternary sedimentary rocks outcrop all along the Manzai Ranges. The mapped thrusts are Khirgi and Jandola. The splay of the Khirgi Thrust and an unknown anticline are also mapped. Both faults are emplaced in older quaternary rocks over the younger in the foreland region. The active tectonic leads to the deformation of the alluvial fans, deflected and entrenched streams.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion  
### Ablation Study  
We perform the ablation study on the VoiceBank+DEMAND dataset to analyze the proposed improvements.
## 6 Conclusions  
The simulation results demonstrate that, compared to traditional partitioning algorithms, the proposed algorithm improves receiver sensitivity by \(0.67\) dBm, \(0.76\) dBm, and \(0.71\) dBm under B2B, weak turbulence, and strong turbulence conditions, respectively. Under weak and strong turbulence conditions, the proposed algorithm achieves an NMSE range of \(10^{-7}\sim 10^{-8}\), compared to the \(10^{-6}\sim 10^{-7}\) range for traditional algorithms. In various turbulence environments, the proposed algorithm consistently outperforms the traditional QPSK partitioning algorithm, indicating superior estimation accuracy. The proposed algorithm consistently outperforms the traditional QPSK partitioning algorithm across various turbulence environments, indicating the superior estimation accuracy of the proposed method. Additionally, the proposed algorithm employs a simplified approach in the frequency offset estimation phase by using absolute value operations instead of multiplication, which significantly reduces algorithm complexity. For \(N=1024\), the complexity of the proposed algorithm is approximately \(27.2\%\) of that of the traditional QPSK partitioning algorithm. Notably, the parallel design of the proposed algorithm effectively balances performance and complexity, making it highly suitable for real-time hardware implementation. This approach enhances DSP processing capabilities in real-time hardware systems and holds significant application value in coherent FSO communication systems.  
Conceptualization, S.Z.; methodology, S.Z. and L.W.; software, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z., K.L. and S.D.; project administration, L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Joint Funds of the National Natural Science Foundation of China (Grant No. U22B2009). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available upon reasonable request from the corresponding authors. The authors declare no conflicts of interest.
## 1 Introduction
\begin{array}{l}S_{0}=W_{Sxx}(\mathbf{\rho},\mathbf{\rho};z)+W_{Syy}(\mathbf{\rho },\mathbf{\rho};z)\\ S_{1}=W_{Sxx}(\mathbf{\rho},\mathbf{\rho};z)-W_{Syy}(\mathbf{\rho},\mathbf{\rho};z)\\ S_{2}=W_{Sxy}(\mathbf{\rho},\mathbf{\rho};z)+W_{Syx}(\mathbf{\rho},\mathbf{\rho};z)\\ S_{3}=i\big{[}W_{Syx}(\mathbf{\rho},\mathbf{\rho};z)-W_{Sxy}(\mathbf{\rho},\mathbf{\rho};z) \big{]}\end{array} \right. \tag{16}\]The Stokes parameter of the receiving end (z \(>\) 0), which may be expressed using Equations (15) and (16), is  
\[\left\{ \begin{array}{ll}S_{0}=&\frac{k^{2}A_{x}A_{y}B_{yx}}{4z^{2}\Delta_{Syx} \sqrt{Q(z)^{2}_{Syx}+T(z)^{2}_{Syx}}}\exp\left[4I_{Sxx}(z)\rho^{2}\right]\exp \left[i4Y_{Sxx}(z)\rho^{2}\right]\\ &+\frac{k^{2}A_{y}A_{y}B_{yy}}{4z^{2}\Delta_{Syy}\sqrt{Q(z)^{2}_{Syy}+T(z)^{2} _{Syy}}}\exp\left[4I_{Syy}(z)\rho^{2}\right]\exp\left[i4Y_{Syy}(z)\rho^{2}\right] \\ S_{1}=&\frac{k^{2}A_{x}A_{y}B_{yx}}{4z^{2}\Delta_{Syx}\sqrt{Q(z)^{2}_{Syx}+T(z) ^{2}_{Syx}}}\exp\left[4I_{Sxx}(z)\rho^{2}\right]\exp\left[i4Y_{Sxx}(z)\rho^{2}\right] \\ &-\frac{k^{2}A_{x}A_{y}B_{yy}}{4z^{2}\Delta_{Syy}\sqrt{Q(z)^{2}_{Syy}+T(z)^{2} _{Syy}}}\exp\left[4I_{Syy}(z)\rho^{2}\right]\exp\left[i4Y_{[PERSON]}(z)\rho^{2}\right] \\ S_{2}=&\frac{k^{2}A_{x}A_{y}B_{yy}}{4z^{2}\Delta_{Syy}\sqrt{Q(z)^{2}_{[PERSON]}+T(z) ^{2}_{[PERSON]}}}\exp\left[4I_{Sxy}(z)\rho^{2}\right]\exp\left[i4Y_{Sxy}(z)\rho^{2} \right]\\ &+\frac{k^{2}A_{y}A_{y}B_{yx}}{4z^{2}\Delta_{Syx}\sqrt{Q(z)^{2}_{Syx}+T(z)^{2} _{Syx}}}\exp\left[4I_{Syx}(z)\rho^{2}\right]\exp\left[i4Y_{Syx}(z)\rho^{2}\right] \\ S_{3}=&i\left\{\frac{k^{2}A_{y}A_{z}B_{yx}}{4z^{2}\Delta_{Syx}\sqrt{Q(z)^{2}_{Syx }+T(z)^{2}_{Syx}}}\exp\left[4I_{Syx}(z)\rho^{2}\right]\exp\left[i4Y_{Syx}(z) \rho^{2}\right]\right.\\ &\left.-\frac{k^{2}A_{x}A_{y}B_{yy}}{4z^{2}\Delta_{Sxy}\sqrt{Q(z)^{2}_{Sxy}+T(z )^{2}_{Sxy}}}\exp\left[4I_{Sxy}(z)\rho^{2}\right]\exp\left[i4Y_{Sxy}(z)\rho^{2} \right]\right\}\end{array} \right. \tag{17}\]  
The DoP of the point on the optical axis at the receiving plane can be set by choosing proper parameters according to Equations (13) and (17).
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 3 Methods  
In this section, a prediction model based on Crossformer for non-stationary time series of RUL (De-stationary-Crossformer) is proposed, considering the multidimensional features and non-stationarity characteristics of the dataset in PEMFC RUL prediction, as illustrated in Figure 3.  
The data initially undergo deep adaptive instance normalization (DAIN) to mitigate non-stationarity and enhance predictability. Subsequently, the Crossformer model is employed as the main architecture to model the dependencies between feature dimensions and time dimensions. This architecture comprises Dimension-Segment-Wise Embedding (DSW Embedding) and an Encoder-Decoder with a Two-Stage Attention Layer. During attention computation, a De-stationary Attention (DSA) mechanism is used to extract non-stationarity information from the raw data. Additionally, handcrafted features are directly introduced from the original input data using mathematical statistics to enhance the predictive performance of the model. During model training, the TILDE-Q loss function is employed to encourage the model to consider the similarity between the predicted sequence and the true sequence, thereby enhancing prediction accuracy while preserving the non-stationarity characteristics of the original data.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview
### Implementation in GNU Radio  
In this section, we describe the structure of the modified _gr-ieee_ 802.11 transceiver implemented in GNU Radio. The transmitter and the receiver are both constituent parts of the transceiver implementation developed by [PERSON] et al. [21].
## 3 Feature Selection
### Sequential Backward Selection (SBS)  
Contrary to SFS, SBS [53; 54] works in the opposite way. SBS starts from a full set of features and sequentially eliminates the worst feature \(x^{*}\) to result in the highest-valued objective function \(J(Y_{k}-x_{i})\). Its algorithm can be broken down into the following steps:  
* Step 1: Start with full set \(Y_{o}=X\)
* Step 2: Eliminate the worst feature \(x^{*}=\text{argmax}[J(Y_{k}-x)]\) with \(x\in Y_{k}\). The objective function is based on the classification rate from a cross-validation test. The mean value of the 10-fold cross-validation test is used to evaluate the feature subset.
* Step 3: Update \(Y_{k+1}=Y_{k}-x^{*}\) and set \(k=k+1\).
* Step 4: Go to Step 2.  
This procedure continues until a predefined number of features are left. SBS usually works best when the optimal feature subset has a large number of features since SBS spends most of its time visiting large subsets.
## 2 Materials and Methods
### Description of the \"Wireless Connector\" System's Demonstrator and Relative Communication Tests
The ADC component included on the ADFMCDAQ2 acquisition board provides the samples of the received I(t) and Q(t) signals, subsequently processed by First-in First-Out (FIFO) systems for modifying the data-flow on 64-bit registers at a 500 MHz frequency. These data are sent to a threshold decision-maker block for reconstructing the symbols on the receiver side. The constellation of received symbols (Figure 7), with 500 Ms/s symbol rate corresponding to 1 Gbit/s (but extendable up to 3 Gbit/s, as previously reported), has been  
Figure 6: Picture of the experimental setup using the Zynq Ultrascale+MPSoC ZCU102 platform baseband system interconnected to the PEM003 radio system, operating in QPSK modulation. Figure 7: Graphic representation of the QPSK constellation related to the received signals, using the Analog Devices software tool IIO Oscilloscope. displayed through a software application (Analog devices IIO Oscilloscope) for extrapolating the transmitted symbol, processed by FPGA and sent to the PC via the UART port.
## 3 Extraction of Echo Envelope Fluctuation Features
### Simulation Study
#### 3.1.2 Simulation Study of Synthetic Echo  
The echo of an active acoustic decoy was simulated by several monostatic hydrophones, where the number of monostatic hydrophones represents the number of main echo highlights. In this study, the synthetic echo was simulated based on the statistical features of echo highlights [19]. The number of highlights is six; the incident pulse is the LFM signal; the frequencies are 10-20 kHz, 20-40 kHz, and 40-80 kHz; and the pulse lengths are 1 ms and 3 ms.  
Figure 4 shows the results of the synthetic echo. Figure 4 compares the fluctuation intensity features of the echo envelope for different frequencies, and Figure 4 compares the STD of the echo envelope fluctuation intensity, correspondingly. The simulation results of 3 ms pulse length are consistent with Figure 4. From the results, we can see that the fluctuation intensity and its STD remain almost stable when the carrier frequencies change. This greatly differs from the results of the benchmark model because the synthetic echo is the superposition of signals from several monostatic hydrophones. The low-frequency modulation features of the echo envelope can be simulated, such as the echo highlights, but the high-frequency modulation features cannot be simulated, such as echo envelope fluctuation intensity. The high-frequency modulation features are caused by body scattering and elastic scattering.  
Figure 4: **(a)** Fluctuation intensity comparison of a synthetic echo envelope with different frequencies; **(b)** STD comparison of synthetic echo envelope fluctuation intensity with different frequencies.  
Figure 3: **(a)** Fluctuation intensity comparison of echo envelopes with different frequencies; **(b)** STD comparison of echo envelope fluctuation intensity with different frequencies.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System Overview
#### 2.2.2 Inside-Beehive Camera  
Typical low-cost endoscope HD cameras with USB interface were used as inside cameras. Each camera has a diameter of 8 mm, a manually regulated LED illuminator, and a semi-rigid USB cable. All inside cameras have been connected to one single-board computer, which is common for all beehives (Raspberry Pi 3B+ model was used). Raspberry Pi transmits the video as the MPEG stream in real time. The video is compressed with MPEG codec in the camera module, so there is no high demand for computational processing power on the computer side or data transition bandwidth, and several cameras can be connected to one single-board computer easily. The main drawback of this solution is the limited length of the USB cable (up to 5 m) and the necessity of an independent cable connection for each beehive. This solution can be applied to a limited number of beehives, provided they are located nearby the common Raspberry Pi module.
## 3 Results  
The results obtained are shown in Table 2. The mean temperature when the teeth were filled at \(-2\) mm was higher with Calamus(r) than with the other systems (40.16 \({}^{\circ}\)C vs. 39.85 \({}^{\circ}\)C and 37.3 \({}^{\circ}\)C) while at \(-4\) mm, the highest temperature was found with Dia-Duo(r) (34.81 \({}^{\circ}\)C) followed by Elements Free(r) (33.73 \({}^{\circ}\)C) and Calamus(r) (32.91 \({}^{\circ}\)C). The Calamus(r) system presented higher temperatures at \(-2\) mm.  
When we compared the different systems, there was no statistically significant difference between filling at \(-4\) mm and \(-2\) mm in the Dia-Duo System(r = 0.213) (Figure 2). There were statistically significant differences between filling at \(-4\) mm and \(-2\) mm for the Elements Free System(r) (\(p\) = 0.001) (Figure 3) and the Calamus System(r) (\(p\) = 0.002) (Figure 4).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **Heat Transfer Coefficient h (W/m\({}^{2}\) K)** \\ \hline Air (natural convection) & 5\(-\)25 \\ Water (forced convection) & 300–600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical values for the convective heat transfer coefficient in water and in air.  
Figure 1: Experimental set up. (**A**,**C**) Positioning of the tooth with respect to the camera and location of the vice. (**B**,**D**) Images of the thermographic camera.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Length** & **Mean** & **\textgreater{}Temperature** & **\textgreater{}Temperature** \\ \hline Dia-Duo & \(-2\) mm & \(37.3\) & \(42.4\) & \(33.4\) \\  & \(-4\) mm & \(34.81\) & \(45.1\) & \(27.8\) \\ Elements Free & \(-2\) mm & \(39.85\) & \(45.2\) & \(34.2\) \\  & \(-4\) mm & \(33.73\) & \(40.3\) & \(29.8\) \\ Calamus & \(-2\) mm & \(40.16\) & \(51.4\) & \(35\) \\  & \(-4\) mm & \(32.91\) & \(42.1\) & \(29.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System averages based on their sealing depth and minimum and maximum temperature values.  
Figure 3: Average temperatures at \(-2\) mm and \(-4\) mm with Elements Free® (\(p=0.01\)).  
Figure 2: Graph of temperatures at \(-2\) mm and \(-4\) mm with Dia-Duo® (\(p=0.213\)).  
If the different systems were compared according to the sealing depth, no statistically significant differences were found between the different systems at \(-\)2 mm (\(p\) = 0.197) (Figure 5) or \(-\)4 mm (\(p\) = 0.620) (Figure 6).  
Figure 4: Average temperatures at \(-\)2 mm and \(-\)4 mm with Calamus® (\(p\) = 0.002).  
Figure 5: Average temperatures at \(-\)2 mm of the three systems compared. The data show no statistically significant differences (\(p\) = 0.197).  
Finally, all systems were examined according to the depth of obturation, and a statistically significant difference was observed in the obturation at \(-2\) mm and \(-4\) mm (\(p\) = 0.000). A temperature difference of about 5 \({}^{\circ}\)C was also found between the obturation at \(-2\) mm and \(-4\) mm (Figure 7).
## 3 Joint Optimization of Cashing and Recommendation
### System Model  
There are \(n\) pieces of content and \(m\) users in the system, the size of content \(i\) is \(w_{i}\), and the cache capacity of the server is \(c\). There are \(m\) users in the system, and the frequency of requests from user \(u\) for content \(i\) is denoted by \(F_{i}^{u}\). For simplicity, \(F_{i}\) is used to denote the frequency of a user's request for content \(i\), which can be counted by the system in actual operation. In practice, the raw frequency is  
\[P_{i}=\frac{F_{i}}{\sum_{j=1,2,\dots,n}F_{j}}, \tag{1}\]  
which denotes the raw request frequency of the user for the content. The recommender system will give a recommendation score \(R_{i}\) for content \(i\) for the user, and when no recommendation is made for content \(i\), the recommendation score \(R_{i}=0\). With the intervention of the recommender system, we will correct the frequency is  
\[\hat{P}_{i}=\frac{F_{i}+R_{i}}{\sum_{j=1,2,\dots,n}(F_{j}+R_{j})}, \tag{2}\]  
which denotes the actual request frequency of the user at this point.  
Figure 1: Illustration of the system model and a simple example. The back-end server provides all the content, while the CDN caches its favorite content for the users.  
For the server, we use \(x_{i}=1\) to indicate that content \(i\) is cached and \(x_{i}=0\) otherwise. Since the number of cached contents must not exceed the server capacity, there is \(\sum_{j=1,2,\ldots,n}x_{i}\leq c\).  
On the other hand, we define the degree of distortion as  
\[D(k)=1-\frac{\sum_{i\in K}P_{i}}{\sum_{i\in K_{best}}P_{i}}, \tag{3}\]  
which denotes the difference between the least \"honest\" recommender and the \"honest\" recommender when recommending up to \(k\) pieces of content. Here, \(K_{best}\) denotes the set of \(k\) contents with the highest probability of being requested, which is the content that should be recommended by an \"honest\" recommender system, while \(K\) denotes the \(k\) contents that the recommender system actually chooses to recommend. To ensure that the distortion of the system's recommendations for user \(u\) is less than its tolerance upper bound \(d_{u}\), we apply the constraint \(D(k)\leq d_{u}\).  
With the intervention of the recommender system, the cache hit rate of user \(u\)'s content request is  
\[H=\sum_{u=1,2,\ldots,m}\sum_{i=1,2,\ldots,n}x_{i}\hat{P}_{i}^{u} \tag{4}\]
## 2 Principle of FTIR Technology
### Reflection Mode  
The ATR-FTIR technology invented by [PERSON] and [PERSON] [33] is a technique that enables direct sample measurement using the IR beam's total reflection phenomenon, which has several advantages over the transmission mode [13]. Compared with the transmission method, the ATR-FTIR technology primarily used to examine the surface of the sample only requires the sample to be placed in tight contact with the surface of the ATR crystal, requiring less effort and time for sample preparation [34]. For these advantages, ATR-FTIR has become a common way mainly for analyzing asphalt materials [35; 36; 37]. In this study,the ATR-FTIR technique is creatively used for measurement of the surface of the aggregates coated with asphalt mortar.