esa-sceva/satcom-chunk-collection huggingface dataset:
# LSH Models in Federated Recommendation
## 4 Comparative Analysis of Experiment
### NCF Federated Recommendation with DP  
Experiments were conducted on NCF federated recommendation with DP, employing overall and stratified sensitivity schemes with various parameters. The federated recommendation performance of three models (GMF, MLP, and NeuMF) was compared under different levels of noise.  
The ratio of negative to positive training samples for all three models was set to 4:1, with a value of K equal to 10. Each model utilized a hidden vector layer of length 8, and the MLP component included a hidden layer with a dimensionality of 10. Five participants were involved in each round. In this experiment, we explored the joint recommendation performance of the NCF model with different differential privacy (DP) parameters. The experiments involved three models, GMF, MLP, and NeuMF, and Laplace noise was introduced to achieve DP. We recorded the HR, NDCG, and F1 scores under different DP parameters \(\varepsilon\). The following results in Table 5 is a summary of the experimental data.  
The results indicate that the three NCF models under the overall sensitivity scheme can maintain a certain level of recommendation accuracy only when \(\varepsilon\) is large, such as setting \(\varepsilon\) to 60. At small \(\varepsilon\), the recommendation accuracy is far below the requirements. However, as \(\varepsilon\) increases, the HR, NDCG, and F1 scores of all the three models improved, but the increase gradually decreased; not only is privacy protection not guaranteed, but the recommendation effectiveness also deteriorates under the overall sensitivity scheme.  
Under the stratified sensitivity scheme, the recommendation effectiveness of GMF improves relatively as \(\varepsilon\) increases. However, it remains less effective than the model without DP. It shows that DP has little effect on the MLP recommendation. And the recommendation effectiveness of NeuCF with DP noise is initially relatively poor, but as \(\varepsilon\) increases, its recommendation effectiveness improves. Overall, as \(\varepsilon\) increases, the recommendation effectiveness of GMF and NeuMF also improves relatively, but this comes at the cost of deteriorating privacy protection. Therefore, to better protect privacy while meeting the recommendation requirements, it is advisable to choose a smaller \(\varepsilon\) if possible, even though recommendations may be less effective. This ensures that models uploaded by clients are well protected.
# Mechanical Analysis of Ground Sleeve
## 2 Experimental Procedure  
### Description of Specimens  
#### 2.1.1 Grouted Sleeve Lapping Connector  
Figure 4 shows the details of the grouted sleeve lapping connector specimens. The specimen comprised two lapped main bars and a grouted-filled sleeve, which was cut from a cylindrical standard steel pipe without any reprocessing. One main bar was spot-welded to the pipe and is, henceforth, referred to as the welded bar. The other main bar, which is referred to as the inserted bar, was placed close to the welded bar and the sleeve pipe-wall. The length of the overlap was the same as the length of the sleeve.  
Being different from the grouted sleeve lapping connector shown in Figure 3, in which the inserted bar is not close to the welded bar, the two bars in this experimental study (as shown in Figure 4) are near to each other and both close to the sleeve pipe-wall. The purpose of this design was to test the mechanical properties of the grouted sleeve lapping connector under the worst-case scenario.  
Steel pipes with internal diameters of 70 mm and thicknesses of 3 mm with a nominal yield strength of 335 N/mm\({}^{2}\) were used to splice the bars. All of the bar diameters were 18 mm. The sleeve lengths, \(L\), were 100, 150, 200, 250, and 300 mm. Each group of a particular lap length contained three or four identical specimens.
## 5 Modelling of Communication Measurement Systems  
### Model Design  
The comprehensive simulation system for communication measurement is built based on Simulink, as shown in Figure 12 below, including transmitting, analog-to-digital conversion, matched filtering, carrier synchronization, symbol synchronization and other modules, which can realize modulation modes such as BPSK, QPSK, OQPSK and QAM [23; 24]. In the transmitter model, the phase noise time-domain generation model in Section 4 is entered into the system, and the coefficients of the phase-noise power law spectrum are changed to generate the original transmit signal containing the phase-noise time-domain sequence with the corresponding amplitude (measured by the root mean square of the noise). The Carrier Synchronization Loop module includes the phase noise transfer model introduced in Section 3, and has the ability to analyze the carrier loop performance under large dynamic conditions between satellites. The sampling rate of the simulation system is variable and can be set to 50 KHz\(\sim\)50 MHz as needed.  
Figure 11: Phase noise power spectral density simulation curve based on Figure 9.  
Figure 12: Simulation model of communication and measurement system.
## 2 Literature Review
### Selection for Structural Component Detection  
As shown in Section 2.2, when the accuracy meets certain baselines, deep learning approaches are continuously developed for the speed of detection. Besides, in some application scenarios of object detection, it is noted that researchers prefer to use deep learning algorithms with a simple structure and faster detection speed. In particular, YOLO and improved algorithms based on YOLO are widely applied. YOLO is an end-to-end model that directly predicts the location of bounding boxes and the class probabilities of objects from the original image. Due to this concise and straightforward detection process, the detection speed of YOLO is extremely fast and the object in video can be detected in real-time. Meanwhile, comparing the two-stage object detection approaches like R-CNN, Fast R-CNN and Faster R-CNN, YOLO has less background errors since it trains on the whole image, which effectively helps to acquire contexture information about the target object. In addition, YOLO has characteristics of quick convergence and strong generalization ability.  
Furthermore, structural components such as beams and columns in structural drawings have several characteristics like small size, high similarity and less features. When detecting these components using a deep learning-based method, a deep convolutional neural network is needed to form more abstract features to represent the location and category information. Moreover, a structural drawing always contains hundreds of components, and there are dozens of such drawings in a construction project. As a result, the detection method is needed to meet the precision for object detection as well as the speed nearly up to real-time level.  
Therefore, according to the analysis mentioned above, YOLO is recommended to use for detecting structural components in scanned CAD drawings.
## 4 Conclusions  
In this paper, we proposed the new waveform GFB-OFDM, which could be a candidate for 6G communication. It utilizes two levels of IFFTs, both in small sizes, and adopts the polyphase filtering to attain the same effect of the corresponding IFFT in the large size, which supports wideband communication. By adjusting the number of subbands for a fixed subband bandwidth, it enables data transmission in different channel bandwidths and thus facilitates a flexible support for various service requirements regarding hardware constraints. It can further adapt to the scenarios with various subcarrier spacings for each subband and accommodate both the single-carrier modulation and the multi-carrier modulation without increasing any computational complexity. We then provided the analysis and the simulation, and they verify that GFB-OFDM demonstrates the aforementioned advantages in the THz high-bandwidth communication and in the varying subcarrier spacing requirements. In future 6G communications that cover numerous complex application scenarios, different performance indicators could vary according to the specific applications. The proposed GFB-OFDM is capable of supporting the characteristic requirements of ultra-wide bandwidth, ultra-high speed, different data services, different subcarrier spacings, and different channel bandwidths in various scenarios and demands.  
Figure 13: The complexity of CP-OFDM and GFB-OFDM is evaluated under a varying number of subbands. Under different numbers of subbands, the number of multiplications required by GFB-OFDM is less than that of CP-OFDM. In addition, as the number of subbands increases, the number of multiplications required by CP-OFDM increases rapidly. In contrast, the number of multiplications required by GFB-OFDM increases slowly.  
**Author Contributions:** Conceptualization, Y.X., J.H. and T.B.; Methodology, Y.X., J.H. and Y.H.; Software, J.H.; Validation, Y.X., T.B., J.H. and F.W.; Formal analysis, Y.H. and F.W.; Investigation, Z.X.; Resources, Y.X. and J.H.; Writing--original draft, J.H. and Y.H.; Writing - review and editing, Y.H. and F.W.; Visualization, X.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the ZTE Industry-University-Institute Cooperation Funds under Grant No. IA20240319006.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors [PERSON], [PERSON] and [PERSON] were employed by the ZTE Corporation, The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from ZTE Industry-University-Institute Cooperation Funds. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Types of Flexible Wearable Antennas
### Textile Antennas  
This study introduces biomedical-optimized tiny patch antennas and a frequency-reconfigurable textile patch antenna for the 2.45 and 5.8 GHz ISM wireless bands [13]. Flexible textile antennas radiate strongly in UWB frequencies and beyond, even under bending conditions. Stable operation is demonstrated with copper-based antennas integrated into textile substrates, which are antennas displaying compatibility with both UWB and WBAN frequencies. Additionally, the bending performance of a dual-band circular textile patch antenna is explored, further validating its applicability for wearable and body-centric applications [14]. The integration of smart materials, e-textiles, smart garments, or garment tracking has enabled smart capabilities in health monitoring and garment tracking. Electrical textiles, leveraging the innovations in conductive polymers since the Nobel Prize-winning development in 1977, have played a pivotal role in the developing e-textile technologies. For wireless detection, recognition, processing, and control, smart garments use textile antennas [15]. Wearable electronics made by fusion, material science, and creative design utilize textile antennas for thermal protection and aesthetics. Copper sheets for conductivity and denim for the dielectric substrate in these antennas exhibit a relationship between the bending radius and resonance frequency, demonstrating the need for flexible, electrically conductive materials in portable antenna design [15]. Wearable antennas designed for WLAN compatibility emphasize form factors and high-performance characteristics. Compared to traditional button antennas, dielectric resonator antennas (DRAs) offer superior compactness, adaptability, enhanced performance, higher gains, and broader bandwidth [16]. A 5.8 GHz, flexible DRA with a dielectric resonator performed well in free space, with phantoms, and near a cell phone with a 5.4 dB gain. However, DRAs are larger than button antennas and exhibit superior radiation characteristics, making them highly suitable for wearable applications [17]. Textiles' antennas have several shapes and sizes in medical applications, as seen in Table 2.
## 3 Materials and Methods
### Questionnaire-MI Performance Canonical Correlation Analysis (QMIP-CCA)  
Let us consider the questionnaire matrix \(\mathbf{\Xi}\in[0,1]^{R\times Q}\) holding \(Q\) informative MI-EEG questions (see Section 3.1) along \(R\) subjects. In turn, let \(\mathbf{\Gamma}\in\mathbb{R}^{R\times A}\) be an MI-EEG classification performance matrix, holding the following measures:  
\[\text{Accuracy}= \frac{TP+TN}{TP+TN+FP+FN} \tag{11}\] \[\text{AUC}= \int_{0}^{1}\frac{\text{TP}}{\text{TP}+\text{FN}}\,d\left(\frac{ \text{FP}}{\text{FP}+\text{TN}}\right)\] (12) \[\text{Kappa}= \frac{2\cdot(TP\cdot TN-FP\cdot FN)}{(TP+FP)\cdot(FP+TN)+(TP+FN) \cdot(FN+TN)}, \tag{13}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) stand for true positive, true negative, false positive, and false negative, respectively. Notably, the accuracy, the area under the curve (AUC), and the Kappa (see Equations (11)-(13)) quantify the DL MI-EEG classification performance [64]. We also compute the aforementioned MI performance measures using, as input, the explanation maps in Equation (9). Consequently, \(A=6\) for a given DL model. Next, we propose to compute the questionnaire and MI-performance inter-subject matching matrices, \(\tilde{\mathbf{\Xi}}\in[0,1]^{R\times Q}\) and \(\tilde{\mathbf{\Gamma}}\in[0,1]^{R\times A}\), as follows:  
\[\tilde{\mathbf{\Xi}}_{\eta}=\frac{1}{R}\sum_{r^{\prime}=1}^{R}\kappa( \xi_{\eta}-\xi_{r^{\prime}q}|\sigma_{q}^{2})=\frac{1}{R}\sum_{r^{\prime}=1}^{R }\exp\left(\frac{-\|\xi_{\eta}-\xi_{r^{\prime}q}\|_{2}^{2}}{2\sigma_{q}^{2}}\right) \tag{14}\] \[\tilde{\mathbf{\Gamma}}_{ra}=\frac{1}{R}\sum_{r^{\prime}=1}^{R}\kappa (\gamma_{ra}-\gamma_{r^{\prime}a}|\sigma_{a}^{2})=\frac{1}{R}\sum_{r^{\prime}= 1}^{R}\exp\left(\frac{-\|\gamma_{ra}-\gamma_{r^{\prime}a}\|_{2}^{2}}{2\sigma_{ a}^{2}}\right), \tag{15}\]  
with \(\xi_{\eta t}\in\Xi\) and \(\gamma_{ra}\in\mathbf{\Gamma};r,r^{\prime}\in\{1,2,\dots,R\}\), \(q\in\{1,2,\dots,Q\}\), and \(a\in\{1,2,\dots,A\}\). The kernel function \(\kappa(\cdot,\sigma^{2})\) is set as a Gaussian similarity with bandwidth \(\sigma^{2}\in\mathbb{R}^{+}\), which is found by taking the median of the Euclidean distances between the kernel input samples as follows: \(\sigma_{q}^{2}=\text{Median}\{\|\xi_{\eta t}-\xi_{r^{\prime}q}\|_{2}:\forall r,r^{\prime}\in R\}\) and \(\sigma_{a}^{2}=\text{Median}\{\|\gamma_{ra}-\gamma_{r^{\prime}a}\|_{2}:\forall r,r^{\prime}\in R\}\).
## 3 Results and Discussion
### Dyes Preparation and Characterization
This figure also includes spectral photon density, \(\Phi_{\lambda}\), calculated from the standard ASTM G173-03 reference solar spectrum derived from SMARTS v. 2.9.2 and provided by Plataforma Solar de Almeria (PSA-CIEMAT, Almeria, Spain). These data provide information to analyze dyes' spectra. Since TiO\({}_{2}\) absorbs at wavelengths below 387.5 nm (semiconductor bad gap \(Eg=3.2\) eV), there is an optical window of interest for dyes in the visible region, from 400 to 750 nm, where the higher the wavelength the higher the number of photons despite them having less energy. Roughly speaking, a dye is presumed to work better when having wide absorption near red wavelengths. The spectrum of N719, the most widely-used artificial dye for DSSCs, is also included in Figure 2 as a reference. As can be seen, N719 has a peak around 400 nm and a wide peak around 550 nm, both of which are in this spectral region. The wavelengths may change slightly, as they depend on dye concentration and pH [37]. The spectrum obtained for pollen with Solution 1 (pollen 1) showed a wide and noisy peak around 350 nm, as was also found by [PERSON] [45]. The spectrum obtained with Solution 2 gives two main peaks at 424 and 456 nm, then absorbance diminishes and slightly increases again for 718 nm up to a scarce 20% of the main peaks. Flavonoids can account for 2-5% of pollen dry weight [2], but the  
Figure 2: Spectra of pollen extracts with Solutions 1 and 2 and artificial dye N719 spectrum. _Right y axis_: Spectral photon density, \(\Phi_{\lambda}\), calculated from the standard ASTM G173-03 reference solar spectrum. specific compounds depend on geographical area [46], and FT-IR spectroscopy or spectral fluorescence analysis should be required to identify them [47; 48]. This would complicate our analysis. Besides, the main peak is found for wavelengths far below the optimum interval of interest. For these reasons, and also because no high efficiencies for pollen dyes have been reported in literature, pollen extracts were not finally used as NDSSC sensitizers. The dye solutions extracted from other vegetable materials were also studied by UV-Vis spectroscopy and results will be shown next. Clearly, the spectra contain pigments from two families: flavonoids (anthocyanins, in eggplant extracts) and belatains (in extracts from betroot and different colored bougainvillea bracts).
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 6 H\({}_{2}\) Generation in DORs
### Concept
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Application Mode** & **Reported Mechanisms** & **H\({}_{2}\)1** & **AR [Sm\({}^{3}\)/m\({}^{3}\)]** & **Ref.** \\ \hline Core tests with reservoir bitumen, T = 280–350 \({}^{\circ}\)C & Thermal cracking, WGS, coke gasification & 1 & n.a & [77] \\ \hline Core sandpack, T = ca. 500 \({}^{\circ}\)C & Partial combustion, WGS & 2.1 & 168 & [81] \\ \hline Pilot scale DHG unit, with Naphta cut, T = 750 \({}^{\circ}\)C & Catalytic steam reforming & 50 & n.a. & [78] \\ \hline Conical combustion cell with Athabasca bitumen & Thermal cracking, LTO/HTO & \textless{}5 \({}^{\circ}\)2 & 400 & [89] \\ \hline Combustion (sandpack) tube, T = 800 \({}^{\circ}\)C & Catalytic combustion, WGS, SG & 1.5 & 220 & [80] \\ \hline Core flood tests, hot water-steam, T = 200 \({}^{\circ}\)C & Decarboxization + the reaction with rock & 11 & n.a. & [90] \\ \hline Experimental, 3-D, SADG followed by ISC & Thermal cracking of bitumen & 1.2 & n.a. & [91] \\ \hline Conical sandpack with T = 400–500 \({}^{\circ}\)C & Partial combustion, WGS & 6 & n.a. & [57] \\ \hline \hline \end{tabular}  
* Production g/kg oil (of OOIP),\({}^{2}\) mol% in total produced gas composition, mass not provided; AR: Air requirement; LTO/HTO: Low-high temperature oxidation; WGS: Water-gas shift; SAGD: Steam assisted gravity drainage.  
\end{table} 
Table 4: List of the ISC and related experiments reporting H\({}_{2}\) generation: application modes, reported mechanisms, H\({}_{2}\) yields, and applied air consumption (AR) data.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 3 Methodology
### Determination of Terrain Parameters  
Noise Propagation Modelling requires noise data of sources, the detail of terrain through which noise propagates to different receiving (or destination) locations, and a prediction model. The noise after generating from source propagates to receiving locations. During its propagation, noise energies are lost due to the distance they travel, interact with buildings, ground, trees, etc. The distance-related losses are termed as Distance Attenuation (DA), while building or barrier-related losses are Barrier Attenuation (BA) [19; 22; 1]. The determination of barrier attenuation depends on determination of 'Route Difference', the extra route a sound wave needs to travel to reach a destination after diffraction.  
\[Route\;difference=D1-D\]  
where \(D1\) = Indirect transmission route, \(D\) = Direct transmission route.  
Figure 11: (**a**) Simulated 3D environment of route determination where green dot show the source and destination symbol (\(\bullet\)). (**b**) Cutting plane results for the simulated region. (**c**) Complete route traversal on left side panel. (**d**) Route traversal on right side panel starting from source. (**e**) Route traversal on left side and right side.  
Figure 12: (**a**) In this figure, reflection point (R’) is found (triangulation exists) between a pair of source and destination, (**b**) “S” is source and “D” is destination, R’ is new reflection point received.  
The noise data at receiver location is computed after accounting all losses in energy in its travel route. The noise level or Sound Pressure Level (SPL) at the receiving location is determined after deducting the loses in sound levels during propagation (attenuations) from the _SPL_ of source location.  
\[SPL(destination)\] \[=SPL(source)-D.A-B.A(db\;sum\;of\;all\;possible\;idirectrouteroute)\] \[\boldsymbol{D.A}=\boldsymbol{20}\;log_{10}\boldsymbol{D}+ \boldsymbol{11}\] \[\boldsymbol{B.A}=\boldsymbol{5.65}+\boldsymbol{66N}+\boldsymbol{244 N^{2}}+\boldsymbol{287N^{3}}\quad\text{if}\quad-\boldsymbol{0.30}\leq\mathbf{N}\leq- \boldsymbol{0.02}\] \[\boldsymbol{B.A}=\boldsymbol{5.02}+\boldsymbol{21.1N}-\boldsymbol{ 19.9N^{2}}+\boldsymbol{6.69N^{3}}\quad\text{if}\quad-\boldsymbol{0.02}\leq N \leq\boldsymbol{1.0}\] \[\boldsymbol{B.A}=\boldsymbol{10}log_{10}\boldsymbol{N}+\boldsymbol{ 18}\quad\text{if}\quad\boldsymbol{1.0}\leq N\leq\boldsymbol{18.0}\] \[\boldsymbol{B.A}=\boldsymbol{25}\quad\text{if}\quad\boldsymbol{N} \geq\boldsymbol{18}\] \[N=\frac{\boldsymbol{Route\;difference}}{\lambda}\] \[\lambda=\frac{c}{f}\]  
where,  
\(D=\) Direct transmission route  
\(D.A=\) Distance Attenuation  
\(B.A=\) Barrier attenuation  
\(\lambda=\) wavelength  
\(c=\) Speed of light  
\(f=\) Frequency  
\(N=\) Fresnel number