esa-sceva/satcom-chunk-collection huggingface dataset:
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 2 Related Work  
### Handcrafted Methods  
The different handcrafted approaches used for palm vein recognition can be categorized into four groups: geometric-based methods, statistical-based methods, local invariant-based methods, and subspace-based methods.  
Geometric-based methods, which use geometric elements such as points, lines, or curves, have been studied as methods with which to extract the palm vessel [6; 11; 12]. However, due to problems such as external light conditions, low contrast, skin scattering, and optical blurring, which generally occur in contactless palm vein images, and which cause difficulties for the extraction of precise vein segmentation. Moreover, the geometry-based methods offer low discrimination, as they suffer directly from rotation, scaling, and translation.  
Statistical-based methods, such as local binary patterns (LBP) [13; 14; 15; 16], modified local binary patterns [17; 18], local texture patterns [19], local tetra patterns [20], and a local directional texture pattern (LDTP) [21; 22], are used to extract the rich texture-based features of blood vessels. Because the pixel-to-pixel image processing technique was used, the aforementioned methods suffered from weak texture and are very vulnerable to image noise as well as sensitive to image rotation and shifting caused by the displaced hands of users. Therefore, they often achieve a low identification rate. Moreover, encoding the palm vessel structure also degrades the feature representation of the local binary pattern.  
Local invariant-based methods [23; 24; 25; 26; 27], such as the scale invariant feature transform (SIFT), speeded-up robust features (SURF), and RootSIFT, can overcome such problems of scale uncertainty, orientation, and translation. Thus, this approach can be considered a competitive handcrafted feature extraction method. However, this results in a lengthy computational time and incorrect verification due to the existence of unstable feature points caused by external factors in low-quality palm images.  
Subspace-based approaches [28; 29; 30; 31; 32; 33; 34] are also proposed to reduce the dimensionality of training data to a lower-dimensional space. These methods include principal component analysis (PCA), linear discriminative analysis (LDA), Fisher linear discriminant (FLD), and independent component analysis (ICA). Because of the manual feature extraction and the fact that non-training-based methods are selected, these feature-based systems are generally time-consuming and error-prone.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results
### Relative Humidity Prediction  
Figure 6 compares the performance of MLTs in living room relative humidity prediction based on IAQ monitoring in the kitchen. The following performance metrics were used: R\({}^{2}\) (Figure 6a), MAPE (Figure 6b), and RMSE (Figure 6c). Their median values are shown in Table 3.  
Figure 5: Comparison of the MLTs’ performance in predicting living room temperature based on IAQ monitoring in the kitchen. The metrics are (**a**) R\({}^{2}\), (**b**) MAPE, and (**c**) RMSE. The dots indicate the means.  
As shown in Figure 6a, linear MLTs offered the best quality in relative humidity prediction. Coefficients of determination were highest in the case of MLR (R\({}^{2}\) = 0.72) and MLP2 (R\({}^{2}\) = 0.71), while the naive approach (R\({}^{2}\) = 0.719) offered a comparable performance. The quality of nonlinear MLTs (DT, RFR, KNN, and MLP1) was very low, as shown by a small R\({}^{2}\) (0.31-0.57). Based on Figure 6b, linear methods MLR (MAPE = 4.42%) and MLP2 (MAPE = 4.18%) yielded the lowest percentage error of RH prediction. In the case of the naive approach, the error was higher (MAPE = 6.60%), and it still increased when applying nonlinear MLTs (MAPE = 6.86-8.01%RH). Regarding the RMSE of RH prediction (Figure 6c), linear methods MLR and MLP2 offered the lowest errors of RMSE (RMSE = 3.27%RH and RMSE = 3.07%RH, respectively). They outperformed the naive approach (RMSE = 4.14%RH) and nonlinear MLTs as with these models, RH prediction was still greater (RMSE = 4.64-6.26%RH). In conclusion, it is possible to predict relative humidity in the living room based on IAQ monitoring in the kitchen with an error smaller than 3.50%RH using linear models. Similar to T, it is crucial to note that this result was achieved by prediction models trained on IAQ monitoring data collected in different apartments and not where the prediction was realized. The best prediction was achieved using MLR, and the quality of this model was the highest. Such a result could not be attained by the naive approach. Nonlinear MLTs were even less effective for RH prediction.
## 5 Results and Analysis
### Refraction Loss  
The internal shape of underground mines and dimensions are rough enough to disrupt the originality of signal and the dielectric medium of mines badly affects the characteristics of signal transmission. Non-uniformity in the medium of mines and refraction by the floor and walls are regarded as refraction loss. Due to the penetration of transmitted signals through any medium, a portion of the signal may be absorbed, resulting in severe refraction loss.  
Refraction loss is calculated by considering both horizontal and vertical medium characteristics, depending on the width and height of a mine. Only the dominant polarization is considered. The defined expression of refraction loss is given in Equations (39) and (40) [7; 37]:  
\[L_{H} =4.343\lambda^{2}\Bigg{(}\frac{\varepsilon_{v}}{w^{3}\sqrt{( \varepsilon_{v}-1)}}+\frac{1}{h^{3}\sqrt{(\varepsilon_{h}-1)}}\Bigg{)}z \tag{39}\] \[L_{v} =4.343\lambda^{2}\Bigg{(}\frac{1}{w^{3}\sqrt{(\varepsilon_{v}-1)} }+\frac{\varepsilon_{h}}{h^{3}\sqrt{(\varepsilon_{h}-1)}}\Bigg{)}z \tag{40}\]  
where \(\lambda\), \(w\), \(h\), \(z\), \(\varepsilon_{v}\), and \(\varepsilon_{h}\) represents the frequency, width of mine, height, distance, the permittivity of sidewalls, and top ceiling, respectively.  
The refraction loss is computed against horizontal and vertical polarization. The resultant sum is displayed in Figure 9 at the operating frequencies of 2.4 GHz and 5 GHz. Figure 9 shows the refraction loss for various link distances using different operating frequencies. There is an exponential rise in the refraction loss as the link distance increases due to the interior surface and room-pillar structure of mines. A higher loss is observed at a higher link range due to a directly proportional relationship with link distance. The relative permittivity of the medium has a minor effect on refraction, but the width and height of mine galleries is an important parameter to consider when measuring refraction loss. If the width and height of the gallery are low, there is a high probability of refraction. The operating frequency signal of 5 GHz is refracting less in the mine environment than that of 2.4 GHz.
###### Abstract  
The aim of this study was to evaluate and characterize the bacterial load present in twenty-four Ready-To-Eat (RTE) sandwiches, purchased at refrigerated vending machines and supermarkets in the province of Modena (Italy). We isolated 54 bacterial strains, including pathogens of interest in food safety, such as _Listeria_, _Staphylococcus_, _Enterococcus_, _Yersinia_, _Aeronomas_ and _Acinetobacter_ spp. Phenotypic tests have been performed on these pathogens to detect the presence of virulence factors, such as gelatinase production and hemolytic capability. To test their antibiotic resistance features, the minimum inhibitory concentration (MIC) against eight commonly used antibiotics (Amikacin, Ciprofloxacin, Ampicillin, Oxacillin, Imipenem, Tetracycline, Erythromycin and Vanomycin) was also evaluated. The results showed that among the 54 isolates, fifty percent (50%) belonged to harmless microorganisms (_Luconostoc_ and _Lactococcus_), whereas the remaining fifty percent (50%) included pathogenic bacteria (_Listeria ivunovii_, _Listeria monocytogenes_, _Staphylococcus aureus_, _Yersinia_, and _Citrobacter_ spp.), species responsible for pathologies often difficult to treat due to the presence of antibiotic resistance features. This study demonstrates the importance of thorough controls, both during the production and marketing of RTE food like sandwiches, to avoid reaching the infectious load and the onset of pathologies, particularly dangerous for old and immunocompromised patients.  
sandwiches; pathogens; _Listeria_; antibiotic resistance; virulence factors
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results
### Experiment Setup and Results  
We have conducted three different experimental scenarios to study the improvements achieved by our proposed framework to service latency. First, we conducted an experiment using a single fog node serving the offloaded resources to a single nearby consumer and measured the latency improvements. Second, we compared the latency improvements evaluated in the first experiment while having the fog node on different network a distance away from the consumer. In other words, the fog node was deployed at different hop counts from the consumer. Third, we have deployed four (4) different fog nodes on the same local network and measured the latency of requesting the services while the resources are offloaded on different fog nodes.
# MINC-NRL: An Information-Based Approach for Community Detection
## 4 Community Detection Based on MINC
### The Generation of the Community Evolution Process  
#### 4.2.1 Network Representation Learning  
The generation of the community evolution process in the approach follows the idea that it simulates a real-world community evolution. To achieve this, the first step is sampling the network to vectorize the nodes, which preserves and reveals the structural information of the network. Such process is also known as network representation learning.  
Multiple methods can be used to learn representations of nodes in network, e.g., neural-network method including Deepwalk [7], Node2 Vec [20], BoostNE [21], Graph-Wave [22], and spectral method such as GLEE [23]. By comparing the results of these methods on our experimental datasets, Deepwalk is finally select in our approach for its balanced and stable result on undirected real-world networks. The details of the comparative experiments are illustrated in Section 5.3.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 4 Experimental Demonstration Wireless OAM Multiplexing Technology using 28 GHz  
### OAM Multiplexing Using Multiple UCAs  
The intensity distributions of OAM signals with different modes are given by the Bessel function of the first kind by their inherent nature, as shown in Figure 7. This yields a selective degradation when the Rx antenna is located in a low SNR region. To address this problem, we used multiple UCAs, which are designed to avoid reception in null regions. In this subsection, we first describe successful experimental results we obtained using five OAM modes over 28 GHz.  
We used multiple UCAs for both Tx and Rx antennas to rectify mode-selective Rx SNR degradation. Figure 8 shows our antenna design. It consists of four UCAs with different radii and a single antenna in the center. Each UCA consists of 16 antenna elements. The gain for each antenna element in each UCA was 11 dBi. The antenna in the center (hereafter UCA No. 0 for notation convenience) is used for the axis alignment and transmission of OAM mode 0. We used the following two methods to choose different UCAs for different OAM modes' transmission and chose Rx UCAs.  
* Antenna Selection: Selecting a single Rx UCA that is not located at the null region of each OAM mode
* Receiver Diversity: Selecting multiple Rx UCAs to obtain Rx SNR enhancement by receiver diversity  
Figure 7: Bessel distributions of Rx signals.
## 2 Improvements and Comparative Analysis of IEEE 802.11 bd
### Improvements of IEEE 802.11 bd to IEEE 802.11p  
The analysis in this section is based on our research of the original texts of the IEEE 802.11 bd [12], 802.11p [6], and 802.11 [28] standards. The IEEE 802.11 bd standard serves as a supplement and enhancement to the existing IEEE 802.11p standard, rather than a replacement, like the NR-V2X standard. The supplement and enhancement part in the 802.11 bd standard is exclusively utilized for communication between NGV stations (STAs) or between NGV STAs and non-NGV STAs. For communication between non-NGV STAs, most specifications in 802.11 bd are still based on the 802.11p standard. In light of the well-established adoption of the 802.11p standard, the 802.11 bd standard was designed to encompass the following fundamental attributes: interoperability, coexistence, compatibility, and fairness. Indeed, a significant portion of the 802.11 bd standard is devoted to realizing the four mentioned characteristics. The remainder of the standard emphasizes the technical improvements and other relevant aspects of 802.11 bd. In summary, 802.11 bd differs significantly from 802.11p, doubling the available bandwidth and introducing changes in frame format, leading to modifications in processing procedures and numerous parameters. From the user viewpoint in the transportation area, this paper focuses on the key technical improvements that can significantly impact the communication performance. The essential technical improvements that NGV STA supports include the following:  
* Integration of enhanced modes for data transmission, specifically 64-QAM 5/6, 256-QAM 3/4, and 256-QAM 5/6;
* Adoption of three LTF formats, NGV-LTF-1x, NGV-LTF-2x, and NGV-LTF-2x-Repeat;
* Implementation of LDPC coding for both transmission and reception;
* Introduction of midamble with periodicities of 4, 8, or 16 OFDM symbols;
* Introduction of the 10 MHz NGV Physical Layer Protocol Data Unit (PPDU);
* Inclusion of NON NGV_10_PPDU and its repetitive variant, the repetitive NON_NGV_10 PPDU.  
The technical improvements that NGV STA may support are as follows:  
* Enabling transmission and reception of Single-User (SU) MIMO with two spatial streams;
* Utilizing the NGV ranging Null Data PPDU (NDP) for NGV ranging;
* Employing 20 MHz NGV PPDU or 20 MHz non-NGV duplicate PPDU.  
In the following two subsections, we will delve into the primary technical improvements of the IEEE 802.11 bd standard. Various V2X performance metrics will guide this exploration. We will also conduct a comparative analysis with the IEEE 802.11p standard to preliminarily evaluate the influence of these improvements on the V2X performance.
###### Abstract  
Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilities by formulating the generation, transmission and distribution of electric energy in advance. However, the nonlinear patterns and dynamics of load data still make accurate load forecasting a challenging task. To address this issue, a deep temporal convolutional network (TCN)-based hybrid model combined with variational mode decomposition (VMD) and self-attention mechanism (SAM) is proposed in this study. Firstly, VMD is used to decompose the original load data into a series of intrinsic mode components that are used to reconstruct a feature matrix combined with other external factors. Secondly, a three-layer convolutional neural network is used as a deep network to extract in-depth features between adjacent time points from the feature matrix, and then the output matrix captures the long-term temporal dependencies using the TCN. Thirdly, long short-term memory (LSTM) is utilized to enhance the extraction of temporal features, and the correlation weights of spatiotemporal features are future-adjusted dynamically using SAM to retain important features during the model training. Finally, the load forecasting results can be obtained from the fully connected layer. The effectiveness and generalization of the proposed model were validated on two real-world public datasets, ISO-NE and GEFCom2012. Experimental results indicate that the proposed model significantly improves the prediction accuracy in terms of evaluation metrics, compared with other contrast models.  
short-term load forecasting; variational modal decomposition; temporal convolutional network; long short-term memory; self-attention mechanism +
Footnote †: journal: Applied Sciences
## 3 Full Description of the Approach  
### Project Framework  
#### 3.1.1 Buildings under Study  
The French real-estate patrimony is rich and all types of buildings can not be taken into account during this project. Hence, it is important to define what type of buildings can be considered. A building is described thanks to several characteristics such as its size, its function, its topology, its date of construction, materials composing it and its aesthetic.  
Buildings taken into account in the developed approach are ordinary buildings both by their function and their aesthetic. The modelling of industrial buildings or historical buildings is not envisaged since these types of buildings require specific processes. Moreover, in the developed approach, a building is described by planar shapes. Complex architectures composed of non-planar shapes are thus set aside. Also, some buildings composed of materials which are difficult to acquire by laser scanning could obviously not be considered. It is notably the case for buildings with facades made entirely of glass. The developed approach deals with buildings of variable sizes and dates of construction. These buildings are composed of one or several floors divided into several rooms. This organisation is followed by the majority of buildings as for example individual houses, public buildings or office buildings. Finally, developed algorithms handle only horizontal ceilings that is why rooms with sloped ceilings are not processed.
## 3 Results and Discussion
### MIMO Performance Analysis
#### 3.4.1. Envelop Correlation Coefficient  
The envelop correlation coefficient for a MIMO configuration can be directly measured using a 3D chamber, or can be calculated using Equation (4), provided in [26].  
\[\left|\rho_{ij}\right|^{2}=\left|\frac{\left|S_{ii}^{*}S_{ij}+S_{ji}^{*}S_{jj} \right|}{\sqrt{\left(1-\left|S_{ii}\right|^{2}-\left|S_{ji}\right|^{2}\right) \left(1-\left|S_{jj}\right|^{2}-\left|S_{ij}\right|^{2}\right)\eta_{rad}\eta_{rad }}}\right| \tag{4}\]  
Equation (4) is used to calculate the value of the ECC between the antenna element \(i\) and \(j\). For 4G wireless devices, the allowed limit for the ECC is 0.3. The envelop correlation coefficient for the mid-band MIMO configuration is reported in Figure 16 and for high-band is reported in Figure 17. The values for the ECC are meaured by using the above-mentioned method. Here, it can be noted that the value of the ECC (measured and simulated) for both configurations is within the allowed operational range for the designed bands.  
Figure 16. The measured and simulated ECC for mid-band MIMO configuration.  
Figure 17. The measured and simulated ECC for mmWave MIMO configuration: **(a)** simulated ECC; **(b)** measured ECC.
## 4 Results and Discussion
5.182 \(\upmu\)m. Figure 11b shows that after ECMG finish machining, the surface was relatively smooth, and the measured surface roughness was 0.760 \(\upmu\)m Ra. Compared with the rough machining, the height fluctuation of the groove surface after finishing was significantly reduced, the surface roughness Ra was reduced by 85.3%, and the surface quality of the workpiece was significantly improved. Figure 12 shows the energy spectrum measurement results (EDX) of the groove surface after rough and finish machining. It can be seen from Figure 12a that the contents of O, C, and Ti elements after rough machining were 36.95 wt%, 8.96 wt%, and 50.04 wt%, respectively. Furthermore, the content of O and C elements decreased significantly after finish machining, which were 1.13 wt% and 3.25 wt%, respectively, while the content of Ti element increased to 87.39 wt%. In addition, the content of Al and V elements also increased. The results show that after the rough-machining stage, due to the stray corrosion of the processed surface of the groove, a large amount of electrolytic products with high adhesion were generated and it was difficult to completely remove these by ultrasonic cleaning. In the ECMG finish machining stage, grinding played an important role, which crushed and removed the electrolytic products covering the processed surface of the groove and thus exposed more metal matrix materials. This further confirmed that no stray corrosion occurred on the surface of the workpiece during the finishing process. Figure 11: Surface morphology (left) and surface roughness (right) of the groove after (**a**) rough machining, and (**b**) finish machining.
## 4 Experimental Settings  
Three popular public corpora were selected for our experimental analysis: LibriSpeech [69], LibriTTS [70], and the voice cloning toolkit dataset (VCTK) [71]. LibriSpeech contains \(\approx\)1000 h of read audiobooks, which were partitioned into \(\approx\)960 h for training and \(\approx\)20 h for evaluation. LibriTTS is a corpus of \(\approx\)585 h of read English speech, specifically designed for TTS and derived from LibriSpeech. The VCTK corpus includes read speech by 110 native English speakers, with each of them providing about 400 sentences. The models were fine-tuned using the LibriTTS _train-clean_ sets and the whole VCTK dataset. The LibriSpeech _test-clean_ and _test-other_ sets (which comprise 4 and 33 speakers, respectively) were used for evaluation. An overview of the dataset partitioning for the training and evaluating of the models is included in Table 1.  
Diverse attack scenarios are discussed in [72], thereby showing the impact of an attacker's knowledge of the achievable protection. Specifically, the strongest attack model (\"informed\") assumed a complete understanding of the speaker anonymization method by the adversary. However, this type of analysis is beyond the scope of the present work. The current work focuses on the simple (\"ignorant\") setup, where the attacker is not aware of the anonymization processing.  
Differently from [26], our ASR setup is based on a Kaldi-based (fixed) model trained on the original LibriSpeech clean signals; although the resulting ASR may exhibit lower performance, the decision to avoid training on anonymized speech was supported by a more equitable comparison across the analyzed conversion techniques, whereby the methods that exhibited a lower acoustic mismatch with respect to the original audio were favored.