esa-sceva/satcom-chunk-collection huggingface dataset:
# Content-Adaptive and Attention-Based Network for Hand Gesture Recognition
## 4 Experiment and Results
### Experimental Setting  
An input video is first sampled using a content-based adaptive sampler. In practice, we set the maximum number of sampled frames per video to 16 and the stride of the sampler to 3. We then resized the shorter side of all sampled frames to 256 pixels, and random crop, color jitter, and random horizontal flip were employed for data augmentation. In the inference phase, only the center crop, resizing, and normalization operations were performed on the input frames. Finally, a center crop of size \(224\times 224\) pixels was used for all frames, thus creating a batch of shape \(x\in\mathbb{R}^{B\times 3\times 16\times 224\times 224}\), where \(B\) indicates the batch size.  
The proposed network was implemented using the PyTorch deep learning framework. The model was trained and validated on a server equipped with two NVIDIA GeForce RTX 3090 GPUs. We adopted a stochastic gradient descent as the optimizer with a momentum of 0.9. We trained the model for 25 epochs with a batch size of 4. The initial learning  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & **Classes** & **Training** & **Validation** & **Testing** & **Total** \\ \hline NVGesture & 25 & 750 & 300 & 482 & 1532 \\ EgoGesture & 83 & 14,416 & 4768 & 4977 & 24,161 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The details of datasets used in our experiments.  
rate was set to 0.001 and then decayed by 0.1 times every 7 epochs. The total trainable parameters of the model are 121.28 million. Such a large model typically a requires larger training dataset to avoid overfitting. To effectively train our models on our two datasets, we employed several regularization strategies, such as data augmentation methods used in data pre-processing. In addition, we used the label smoothing mechanism [31, 45] to regularize the model by estimating the marginalized effect of label dropout during training. During the testing phase, the setup is the same as in the validation phase.
## 3 Discussion
Therefore, physical-mechanical methods, such as surface scrubbing and the use of antimicrobials, must be combined to address this problem. To effectively combat infections resulting from biofilms formation, various methods have been developed covering different aspects, such as: (i) changing the properties of susceptible surfaces to prevent biofilm formation; (ii) regulating signaling pathways to inhibit biofilm formation, and (iii) applying external forces to eradicate the biofilm. An excellent discussion of the applicability and limitations of these methods can be found in recent reviews [35; 36; 37]. Regarding changing surface properties that prevented bacterial contamination of the surface, [PERSON] et al. [38] presented four categories of promising methods: anti-adhesive, contact active, biocide attached/biocide release, and topological modification. Particular attention was paid to antibiotic-free antibacterial strategies. It is well known that biofilm formation is regulated by several signaling pathways, such as quorum sensing (QS) and nucleotide second messenger systems. Therefore, biofilm inhibitors can be designed by targeting relevant proteins in the respective regulatory systems to block their signaling pathways and thereby inhibit biofilm generation. To date, quite a few QS signaling molecules have been described, including autoinducer peptide (AIP), N-acyl homoserine lactones (AHLs), autoinducer 2 (AI-2), Pseudomonas quinolone signal (PQS), diffusible signal factor (DSF) [39]. Among them, AIP, AHLs, and AI-2 are arguably the most widely studied. The regulation of biofilm formation by c-di-GMP, c-di-AMP, and (p)ppGpp, and the corresponding inhibitors that have been developed to control the respective regulatory processes, was also studied against biofilm formation. Several different physical and biochemical approaches as external pressures to eradicate biofilm formation were proposed. For example, physical methods, such as treatment with ultrasound and magnetic fields, can be applied to eradicate biofilms effectively. These external physical factors are simple and crude, but they can only eliminate biofilm to a certain extent. These methods are suitable for decontamination in the food industry, biofilm control in dentistry, and removal of biofilms adsorbed on tissue implants and medical devices, but cannot be used for infected tissues that require more gentle methods such as the use of phage lysines, degrading enzymes, or plant metabolites [40; 41]. Although there are many strategies to control harmful biofilms, due to the complex composition of this microbial structure and its high resistance to stress, it is difficult to remove biofilm with a single method completely. Therefore, using a \"mixed approach\" by combining different strategies may be more effective in eradicating biofilms from different surfaces. We believe that understanding the details of the process of biofilm formation and regulation of its signaling pathways will enable the discovery of new targets that can be used to develop highly effective protocols, and future biofilm control research must be multidisciplinary.
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
###### Abstract  
Recently, multi-frequency multi-constellation receivers have been actively studied, which are single receivers that process multiple global navigation satellite system (GNSS) signals for high accuracy and reliability. However, in order for a single receiver to process multiple GNSS signals, it requires as many code generators as the number of supported GNSS signals, and this is one of the problems that must be solved in implementing an efficient multi-frequency multi-constellation receiver. This paper proposes an area-efficient universal code generator that can support both GPS L1C signals and BDS B1C signals. The proposed architecture alleviates the area problem by sharing common hardware in a time-multiplex mode without degrading the overall system performance. According to the result of the synthesis using the CMOS 65 nm process, the proposed universal code generator has an area reduced by 98%, 93%, and 60% compared to the previous memory-based universal code generator (MB UCG), the Legendre-generation universal code generator (LG UCG), and the Weil-generation universal code generator (WG UCG), respectively. Furthermore, the proposed generator is applicable to all Legendre sequence-based codes.  
universal code generator; Legendre sequence; multi-constellation; GPS L1C; BDS B1C
###### Abstract  
As a significant manifestation of human activities influencing natural environment, rapid urbanization has enhanced economic prosperity while simultaneously posing threats to ecological quality. Beijing-Tianjin-Hebei (BTH), the core region of the Yangtze River Delta (CYRD), and the Pearl River Delta (PRD) stand as three major economic centers characterized by the highest level of urbanization in China, encompassing areas of heightened ecological sensitivity. Nevertheless, the ecological quality at the scale of urban agglomerations remains ambiguous, with many studies failing to develop a comprehensive and effective method for comparing diverse urban agglomerations. Consequently, this study integrates multi-source remote sensing data, including information on land cover and other socio-economic parameters, to construct the Ecological Quality Index (EQI) based on the \"Function-Interaction-Pressure-Stability\" (FIPS) framework. Through a stratified determination of indicator weights grounded in both objective importance and empirical knowledge, we mapped the spatiotemporal changes of EQI and analyzed the impact of urbanization on ecological quality in three urban agglomerations from 2001 to 2020. We determined the following: (1) The calculated EQI can further capture the nuanced details with better performance at both underlining the discrepancy of highs and lows of EQI and describing the spatial detail of urban agglomerations' characteristics. (2) Substantial disparities in EQI and its changes are evident across different urban agglomerations. Notably, only the average EQI improves in PRD, while ecological degradation is prominent in specific regions, such as the southeastern plains of BTH area, along the Yangtze River, and around Shanghai in CYRD and central PRD. The CYRD exhibits the largest affected area. (3) Urbanization predominantly influences ecological quality through land cover transitions. In expansion areas, ecological deterioration is significantly more pronounced, constituting approximately 90% of the total area. (4) Despite significant urbanization, city-level analysis in CYRD reveals a better coordination between urban expansion and ecological protection, with a lower intensity of ecological degradation compared to urban expansion rates. Conversely, some cities in the BTH, despite modest urban expansion, exhibit substantial declines in ecological quality, highlighting the need for targeted policy interventions. In conclusion, this study elucidates the intricate relationship between urbanization and ecological quality, offering valuable insights for the development of targeted protection strategies and sustainable urban planning.  
ecological quality; evaluation framework; synergistic index; urban expansion; spatial heterogeneity; cold/hot spot analysis 2023
## 2 Data and Methods
### Data
#### 2.1.2 Remote Sensing Data  
We used Landsat surface reflectance (SR) and land surface temperature (LST) as well as a number of indices derived from the former as the main predicting features for our machine learning model. Landsat 5, 7, and 8 (TM, ETM+, and OLI respectively) cover our study period and have a 30 m spatial resolution. USGS provides open access to their Landsat Collection 2 Level-2 Science Products (L2 SP) through Amazon s3 cloud service. L2 SP data are geometrically and atmospherically corrected using the LEDAPS algorithm for TM and ETM+ and LaSRC for OLI imagery and thus prepared for scientific analyses. USGS Landsat was accessed from [[https://registry.opendata.aws/usgs-landsat](https://registry.opendata.aws/usgs-landsat)]([https://registry.opendata.aws/usgs-landsat](https://registry.opendata.aws/usgs-landsat)) (accessed on 1 February 2022).  
We filtered out imagery with cloud cover above 75% of a scene. To ensure consistently high data quality, we masked out all possible artifacts as marked in the quality assessment bands provided with the product. We also left out high- to low-confidence clouds and cloud shadows detected by the CFMask algorithm [35].  
We utilized SR from three visible, near-infrared (NIR), and short-wave infrared (SWIR 1 and SWIR 2) spectral bands (bands 1-5 and 7 for TM and ETM+; and 2-7 for OLI). These bands are well-suited for assessing vegetation and soil state and, consequently, together with LST, can serve as accurate predictors of NEE.  
In addition, we selected two spectral indices and two tasseled-cap components [36] to map proxy land surface parameters to predict NEE better. Soil-adjusted vegetation index (SAVI) and tasseled-cap greenness (TCG) reflect vegetation intensity and chlorophyll content. In contrast, normalized-difference moisture index (NDMI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and tasseled-cap wetness (TCW) reflect moisture content in vegetation in our areas of interest [37; 38].  
\[TCG=-0.1603\cdot blue+0.2819\cdot green-0.4934\cdot red+0.7940\cdot NIR-0.0002 \cdot SWIR-0.1446\cdot SWIR2 \tag{1}\]  
\[TCW=0.0315\cdot blue+0.2021\cdot green+0.3102\cdot red+0.1594\cdot NIR-0.6 806\cdot SWIR-0.6109\cdot SWIR2 \tag{2}\]  
\[NDMI=\frac{NIR-SWIR}{NIR+SWIR} \tag{3}\]  
\[NDVI=\frac{NIR-red}{NIR+red} \tag{4}\]  
Figure 1: Locations of all Fluxnet sites.  
\[SAVI=\frac{NIR-red}{NIR+red+0.5}\cdot 1.5 \tag{5}\]  
\[EVI=2.5\cdot\frac{NIR-red}{NIR+6\cdot red-7.5\cdot green+1} \tag{6}\]  
where _blue, green, red_, \(NIR\), \(SWIR\), \(SWIR2\) are Landsat spectral bands of the corresponding wavelengths.
# Metasurface-Based Polarimeters
## 2 Fundamentals
### PB-Phase  
In the previous examples, phase discontinuity is introduced by varying the meta-atoms' geometric parameters. Another completely different technique, known as PB-phase or geometric phase, achieves full phase control of the cross-polarized light by using anisotropic meta-atoms with identical geometry, but spatially varying the orientations for circularly polarized (CP) light [60; 61; 62; 63; 64; 65]. The concept behind PB-phase can be easily revealed by the Jones matrix [66]. In general, the Jones matrix of an anisotropic meta-atom rotated within the \(x\)-\(y\) plane can be written as:  
\[M_{\theta}=R(-\theta)\left(\begin{array}{cc}M_{xx}&0\\ 0&M_{yy}\end{array}\right)R(\theta), \tag{3}\]  
where \(M_{xx}\) and \(M_{yy}\) are the reflection (or transmission) coefficients for LP light along the two axes of the anisotropic meta-atom, \(\theta\) is the rotated angle with respect to the \(x\)-axis, and \(R(\theta)\) is the rotation matrix [15]:  
\[R(\theta)=\left(\begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array}\right). \tag{4}\]  
When the incident light is CP, the reflected or transmitted light can be written as  
\[M_{\theta}\cdot\mathbf{E}_{0}^{\pm}=\frac{1}{2}\left(M_{xx}+M_{yy}\right) \mathbf{E}_{0}^{\pm}+\frac{1}{2}\left(M_{xx}-M_{yy}\right)e^{\pm i2\theta} \mathbf{E}_{0}^{\mp}, \tag{5}\]  
where \(\mathbf{E}_{0}^{\pm}\) represents the incident left-handed CP (+, LCP) and right-handed CP (-, RCP) light [15; 17]. The first term in Equation (5) represents CP light with the same handedness as the incident wave, while the second term stands for CP light with the opposite handedness and gains an additional PB-phase of \(\pm 2\theta\), whose sign depends on the handedness of the incident light. Thereby, \(2\pi\) phase coverage can be achieved if the meta-atom is rotated from 0 to \(180^{\circ}\).
## 2 Methodology
### Model Performance Assessment  
The literature offers a range of statistical performance metrics. In this study, the developed models were evaluated using three distinct statistical methods to assess their prediction accuracy: the Correlation Coefficient (R) (Equation (10)), Root Mean Square Error (RMSE) (Equation (11)), and the Nash-Sutcliffe efficiency (NSE) coefficient (Equation (12)).  
In Equations (10)-(13), \(SPI_{pi}\) = the predicted value, \(SPI_{oi}\) = the observed value, \(N\) = the number of data, \(\overline{SPI_{o}}\) = average observed value, and \(\overline{SPI_{p}}\) = average predicted value.  
The Correlation Coefficient (\(R\)) is given in Equation (10) [95]:  
\[R=\frac{\sum_{i=1}^{N}\left(SPI_{pi}-\overline{SPI_{p}}\right)\left(SPI_{oi} -\overline{SPI_{o}}\right)}{\sqrt{\sum_{i=1}^{N}\left(SPI_{pi}-\overline{SPI _{p}}\right)^{2}}*\sqrt{\sum_{i=1}^{N}\left(SPI_{oi}-\overline{SPI_{o}} \right)^{2}}} \tag{10}\]  
RMSE is given in Equation (11) [96]:  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=0}^{N}\left(SPI_{oi}-SPI_{pi}\right)^{2}}} \tag{11}\]NSE is calculated in Equation (12):  
\[NSE=1-\left[\frac{\sum_{i=1}^{N}\left(SPI_{oi}-SPI_{pi}\right)^{2}}{\sum_{i=1}^{N }\left(SPI_{oi}-\overline{SPI_{o}}\right)^{2}}\right] \tag{12}\]  
A considerably more significant comparative result was obtained by using an RMSE-Standard Deviation Ratio (RSR), which was derived in Equation (13) [97].  
\[RSR=\frac{\sqrt{\sum_{i=}^{N}\left(SPI_{oi}-SPI_{pi}\right)^{2}}}{\sqrt{\sum_{i= }^{N}\left(SPI_{oi}-\overline{SPI_{o}}\right)^{2}}} \tag{13}\]
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.5 Resisting Fog-Node Masquerading Attacks  
During the login and authentication phase of proposed protocol, fog-node \(\mathcal{FN}_{f}\) transmits a message \(\{M_{1},M_{2},TID_{k},TID_{f},auth_{k},auth_{f}\}\) towards cloud service provider \(\mathcal{CSP}\). As the message is transmitted over a public channel, \(\mathcal{A}_{att}\) can intercept this message and try to impersonate the \(\mathcal{FN}_{f}\). Therefore, in order to impersonate the fog-node \(\mathcal{FN}_{f}\) successfully, \(\mathcal{A}_{att}\) needs to re-generate the message \(\{M_{1},M_{2},TID_{k},TID_{f},auth_{k},auth_{f}\}\). It is obvious from Section 4.1.4 that \(\mathcal{A}_{att}\) cannot compute a valid \(auth_{k}\), as it involves the \(ID_{k}\) of \(\mathcal{U}_{k}\), which is unknown to \(\mathcal{A}_{att}\). Likewise, the computation of \(auth_{f}\) is also not possible for \(\mathcal{A}_{att}\), due to the involvement of \(ID_{f}\). Therefore, it is quite impossible for \(\mathcal{A}_{att}\) to re-generate the message \(\{M_{1},M_{2},TID_{k},TID_{f},auth_{k},auth_{f}\}\). So, the impersonation of \(\mathcal{FN}_{f}\) is not possible in our proposed protocol.
# Resurgence of a Nation's Radiation Science Driven by Its Nuclear Industry Needs
### Stand-Alone Analytical Equipment  
In addition to the irradiation capability and in situ analytical capability, DCF houses a suite of ex situ analytical equipment. Gas chromatography is used extensively for quantifying gaseous products of radiolysis. Two gas chromatographs from SRI instruments and an Agilent 7890B are set up for different applications. One SRI gas analyzer utilizes the crush tube method [22], and the other is set up for manual injection of samples taken from off-the-shelf Swagelok components [23]. The Agilent 7890B is fitted with a PAL3 autosampler allowing rapid analysis of headspace from crimp-cap vials.  
Where radiolysis in confined media is of interest, porous media can be characterized using a combination of techniques. A Micromeritics Autopore V can be used for mercury intrusion porosimetry while a Micromeritics TriStar II 3020 can be set up with one of several gases for the analysis of pores down to 2 nm. Complementary analysis can be achieved using a Micrometrics AcuPyc II 1340 H\({}_{2}\) pycnometer. Gas chromatography has been used in conjunction with nitrogen adsorption to investigate radiolytic hydrogen production from other heterogeneous porous media [24]. Pore structures can be further characterized using scanning electron microscopy (Quanta 250 FEG SEM) which is fitted with an energy dispersive X-ray (EDX) spectroscope to aid in determining local chemical differences in a material. EDX has been used to successfully identify the morphology of calcite which formed in cementitious media after irradiation [25]. Further structural characterization is provided by an Empyreon X-ray diffractometer (Malvern Panalytical) and, along with nitrogen adsorption and EDX, has been used to determine microstructural changes in concrete [26].  
Further spectroscopic techniques are available to determine radiologically produced chemical changes. Using a Ram II FT coupled to Vertex series FTIR (Bruker), a combination of Raman and Fourier-transform Infra-red (FT-IR) spectroscopy has been used to detect cross-linking and chain scission in a variety of epoxy resins [27] and to establish a link between radiation chemistry and mechanical properties of a variety of 3D printed plastics [28]. Light spectroscopy (Agilent Technologies Cary Series UV-VIS-nIR) has been used for the quantitative measurement of NO\({}_{2}\)\({}^{-}\) and HNO\({}_{2}\) via the Shinn method, which may commonly be found as a result of radiolysis of nitric acid used in the extraction of useful fission products from used nuclear fuel [29]. Electron paramagnetic resonance (EPR) is available (Bruker EMX nano) to determine radical formation and has been used in conjunction with UV-VIS-nIR spectroscopy to elucidate the mechanisms that lead to unusual radiation-induced color change in glass [30].
# Smart Textiles: A Review and Bibliometric Mapping
## 3 Results and Discussion
### Performance Analysis
#### 3.1.2 Analysis of Oldest and Most Cited Documents
Although the development of PCMs dates back to 1980, when they were developed for spacesuits and gloves, researchers are still struggling to achieve adequate durability of PCMs under repeated use for consumer products [28]. New processes such as electrospinning provide for the formation of nanofibres that have secondary structures, such as a porous, hollow, or core-sheath structures, and can be further functionalised with molecular species or nanoparticles during or after the electrospinning process [99]. Furthermore, they can be used for energy storage, sensors, or wearable/flexible electronics. The development of smart textiles that generate electric power from ambient sunshine and mechanical motion could be the next generation of wearable electronics [97]. The hybrid power textile combines triboelectric nanogenerators, which convert human biomechanical motion into electricity, and a photovoltaic textile, which gathers power from absorbed solar irradiance. Such hybrid power textiles ensure simultaneous harvesting of solar and mechanical energy [97]. The development of health and healthcare-related apps for smartphones supports healthcare and public health interventions by collecting important data for healthcare research, which is also sought by wearable health monitoring systems [98]. Several of the most frequently cited documents are reviews on both commercially available and research prototypes of wearable health monitoring systems [45; 95; 100; 101]. Biosensors can measure various physiological parameters, such as heart rate, blood pressure, body and skin temperature, oxygen saturation, respiration rate, electrocardiogram, etc., in a non-invasive and unobtrusive way. Sensor modules can be integrated into clothing or embedded in garments [95]. [PERSON] et al. [45] looked at the materials and manufacturing process in the production of smart textiles and highlighted a possible trade-off between flexibility, ergonomics, low power consumption, integration, and autonomy. Several issues arose in the use of textile sensors, such as energy harvesting, the use of a wearable power supply, washability, and the change in tactile properties of textiles, such as stretch recovery, drape, shear, and handle [45; 100], but the major issues in wearable healthcare systems remain privacy and security of the user's sensitive medical data, reliable communication links, robust data compression algorithms and energy efficiency [100], as well as the development of intelligent signal processing, data analysis and interpretation, interoperability of communication standards, efficiency of electronic components, and energy supply [101]. In a comprehensive examination of the most cited documents, it is noticeable that the sustainability aspect of smart textiles is missing.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 4 Data and Methodology
### Forecasting Modeling Method  
As shown in Figure 5, firstly, we need to set up the size of window and the step we want to forecast. Secondly, we slide the window forward and obtain a new window. Then, we can use the new window to make a new prediction. Every time we slide the window, we input the new window into the model to obtain a forecasting result.  
In this paper, we select the window size by conducting a grid search among [10; 15; 20; 25; 30]. The 20 that achieves the best performance over the validation set is used for the test.Then, we assign time step 1, 3, 5, and 7 to evaluate the performance of our models in different forecasting steps. It means that we use previous 10 h time series data to predict electricity load in the following: 0.5 h, 1.5 h, 2.5 h, and 3.5 h. We compare the forecasting results between different steps to observe the performance of the models.