esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 4 Building Retrofit Measures and Major Affecting Elements in Shanghai
### Building Retrofit Measures in Shanghai
#### 4.1.4 Low-Carbon/Renewable Energies  
Unlike the previously mentioned technologies, these retrofit technologies are classified under supply-side management (see Table 3). Solar/PV-assisted units and integrated wind turbines are the notable renewable energy systems retrofitted across all building types. They are speculated to account for about 10-15% of the retrofit measures across all buildings. Their energy-saving potential has been improved, especially when applied with given building insulations [57; 63; 66]. They have recently been adopted in Shanghai, but a great percentage of existing buildings are yet to implement these measures, the reason being that the associated costs are high costs with a long payback period. However, supporting policies and incentives can influence supply-side management in circumventing these setbacks [66]. Other notable low-carbon energy sources recommended for the city are gas boilers and heat pumps [68; 70].
# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 2 Materials and Methods
Mathematically, the algorithm determines the maximum and minimum values to define a square with the coordinates  
\[(\max(x_{i}-d_{i}),\max(y_{i}-d_{i})) \tag{19}\]  
and  
\[(\min(x_{i}+d_{i}),\min(y_{i}+d_{i})) \tag{20}\]  
The central point of this square corresponds to the estimated position of the unknown node. Lastly, another localization method is the Maximum Likelihood Estimation (MLE)method, which analyzes the measurements obtained from the reference nodes to estimate the position of an unknown node. Its main objective is to minimize the mean squared error (MSE) between the measured and actual distances, which may be affected by noise. To begin, the distance \(d_{i}\) from each anchor node is calculated from the RSSI values. Then, an error \(e_{i}\) is defined as the difference between the measured distance and the actual distance between the unknown node and each anchor node as follows:  
\[e_{i}(x_{0},y_{0})=d_{i}-\left((x_{i}-x_{0})^{2}+(y_{i}-y_{0})^{2}\right) \tag{21}\]  
where \(b=(x_{0},y_{0})\) is the position of the unknown node, and \((x_{i},y_{i})\) is the position of the \(i\)-th anchor node. This method estimates the position of the unknown node by minimizing \(e_{i}\). The position estimate \((x_{0},y_{0})\) of the unknown node is obtained by solving a system of equations. The estimate, calculated using the MMSE estimation, is the solution to the linear system  
\[y=Xb \tag{22}\]  
where  
\[X= \begin{bmatrix}2(x_{k}-x_{1})&2(y_{k}-y_{1})\\ \vdots&\vdots\\ 2(x_{k}-x_{k-1})&2(y_{k}-y_{k-1})\end{bmatrix}  \tag{23}\]  
and  
\[y= \begin{bmatrix}-x_{1}^{2}-y_{1}^{2}+d_{1}^{2}-(-x_{k}^{2}-y_{k}^{2}+d_{k}^{ 2})\\ \vdots\\ -x_{k-1}^{2}-y_{k-1}^{2}+d_{k-1}^{2}-(-x_{k}^{2}-y_{k}^{2}+d_{k}^{2})\end{bmatrix}  \tag{24}\]  
Once these values are obtained, the estimated position of the unknown node can be calculated using the formula  
\[b=(X^{T}X)^{-1}X^{T}y, \tag{25}\]  
This method works well when a sufficient number of measurements is available, but if only three reference nodes are present, the results may not be accurate.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 6 Discussion  
To our knowledge, this was the first empirical study to investigate integration factors in e-health laboratory adoption, especially for cases in Indonesia. The integration factor in this study was obtained by integrating the TTF and D&M models, and considering additional variables in the form of technology-driven and human-driven technology acceptance models to determine user adoption of the e-health laboratory system. Our results indicate, except for Hypotheses 4, 9, and 12, that our hypothesis was fully supported. Our research model explained 54.7% of the variation in TTF by task characteristics and technology characteristics. Compared to other investigations exploring TTF in the adoption of m-banking [51, 75], m-commerce [76], and social networking sites [81], these results showed good predictive power. The positive effects of technology characteristics and task characteristics on TTF were very clear and confirmed results similar to all previous studies. The perceived usefulness of online adoption of laboratories in our model was explained by TTF, information quality, accessibility, design, and personal innovativeness. This model explained 49.2% of variations in the perceived usefulness of online adoption of laboratories. Our hypothesis stemmed from TTF, the quality of information, accessibility, and personal innovativeness to explain the significant relationship to perceived usefulness in e-health laboratories. For positive and significant information quality results, it was in line with the results of the study [70, 88, 89]. Positive results and significant accessibility to perceived usefulness in this study differed from the results of the study [94, 96] which obtained the opposite results. The results of this study differed from studies [106] for personal innovativeness that provided positive and significant results on perceived usefulness. The design did not support this model, as negative and insignificant results were obtained. This result was different from previous studies [71]. TTF results that had a significant effect on perceived usefulness were similar to the results of the study [77, 78, 79, 37, 52], but these results differed from [118]. There were two reasons for the strong relationship between TTF and perceived benefits [118]. The first was that users may feel that the use of e-health laboratories was highly dependent on the compatibility between health checks experienced with e-health laboratory functionality. The second reason might be that users had a better and clearer understanding of their medical examinations, and thus could directly assess the suitability of e-health laboratory functionality with these tasks. In addition, for the perceived ease of use in e-health laboratories in our model, it was also explained by TTF, information quality, accessibility, design, and personal innovativeness. This model explained 67.2% of the variation in the perceived ease of use in e-health
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 2 Methodology and the Optimization Procedure
### Equivalent Circuit Model of the Inductive Coupling Method  
Figure 2 is the equivalent circuit model of inductive coupling, where, \(V_{AC}\), \(R_{s}\), \(R_{T}\), \(C_{T}\), and \(L_{T}\) represent supply voltage, source resistance, stray resistance, stray capacitance, and primary inductance, respectively. The receiver side has similar components, such as secondary inductances (\(L_{R1}\) and \(L_{R2}\)), stray capacitances (\(C_{R1}\) and \(C_{R2}\)), stray resistances (\(R_{R1}\) and \(R_{R2}\)), and load resistances (\(R_{L1}\) and \(R_{L2}\)). Electrical resonance is a phenomenon that happens in an electric circuit at a specific frequency called the resonant frequency when the fictitious parts of the impedances or admittances of the circuit elements cancel each other out. A circuit's response is optimum at this specific resonant frequency. Any circuit with an inductor and a capacitor can experience resonance since both of these components have the capacity to store energy [14].  
Figure 1: A schematic diagram representing the placement of the coils in and above the heart.  
Energy oscillates between the inductor and capacitor in a resonant circuit, and the rate at which energy is transferred between these two components relies on the values of L and C. We will observe oscillations in the circuitry as a result of these oscillations in energy transmission. If this circuit is perfect and there are no resistive parts of any type, these oscillations will never stop. However, in practice, there will be some resistance present in every circuit. These oscillations will deteriorate as a result of the presence of this resistance. We need to provide this LC circuit with power from an external source at the same frequency to maintain these oscillations (addressed as resonant frequency). We can continue the oscillations in this manner. The two primary fundamental resonance circuits are parallel and series RLC circuits. A concept called the quality factor (Q-factor) can be used in resonance circuits to gauge how sharp the resonance is [15]. A dimensionless metric called the Q-factor is used to determine how effectively a system oscillates. A high Q-factor means that oscillations have more energy and travel farther, while a low Q-factor causes oscillations to weaken more quickly. The frequency needs to be near the real resonant frequency in order to attain a high Q-factor (smaller bandwidth). Hence, a high Q-factor has more energy but is more challenging to tune [16].  
The Q-factor is given in simple terms by Equation (1):  
\[\text{Q}=2\pi\times\frac{\text{maximum stored energy}}{\text{dissipated energy in one period at resonance}}. \tag{1}\]  
The resonant frequency can be determined using Equation (2):  
\[f_{s}=\frac{1}{2\pi\sqrt{LC}}, \tag{2}\]  
where \(L\), \(C\), and \(f_{s}\) represent the inductance, capacitance, and resonant frequency, respectively [1].
# The Use of Rapeseed Husks to Remove Acidic and Basic Dyes from Aquatic Solutions
## 3 Results and Discussion  
### Characteristics of RH (FTIR, Surface)  
The FTIR spectrum of rapeseed husks (Figure 1) was typical of the lignocellulosic plant biomass. Peaks in the range of 1400-900 cm\({}^{-1}\) were characteristic for bonds and functional groups of polysaccharides present in the material [26]. The peaks visible at 1024 cm\({}^{-1}\); 1048 cm\({}^{-1}\); 1144 cm\({}^{-1}\); and also 1201 cm\({}^{-1}\) may be ascribed to the C-O-C bond found in the aromatic rings of cellulose and hemicellulose [27]. In turn, peaks at 956 cm\({}^{-1}\) and 896 cm\({}^{-1}\) indicate the bending of the C-O-H bond of \(\beta\)-glucans [28]. The absorption bands at 1420 cm\({}^{-1}\) and 1371 cm\({}^{-1}\) correspond to the tensile and bending vibrations of -CH\({}_{2}\) bonds of cellulose [29]. The presence of lignin in the analyzed material may be indicated by a peak visible at 1236 cm\({}^{-1}\) corresponding to the C=C bond of the guaiacol aromatic ring and also a peak at 1316 cm\({}^{-1}\) assigned to the stretching of the C-O bond of the syringe [30].  
A wide band noticeable at 3500-3000 cm\({}^{-1}\) denotes the stretching of the O-H bond of hydroxyl functional groups. The peak visible in this band at 3278 cm\({}^{-1}\) corresponds to the symmetrical stretching of the N-H bond (amide A) of biomass proteins [31]. The protein content of rapeseed husks may also be indicated by peaks visible at 1546 cm\({}^{-1}\) and 1266 cm\({}^{-1}\), which are ascribed to N-H bonds of secondary and tertiary amides [32]. A distinct peak visible at 1630 cm\({}^{-1}\) indicates the presence of carboxylic groups in the analyzed material [33]. In turn, the absorption band at 1730 cm\({}^{-1}\) is attributed to the carbonyl group C=O of the ester bond, which may be related to the content of fatty acids, glycerides, and pectins in rapeseed husks [34]. Peaks at 2924 cm\({}^{-1}\) and 2853 cm\({}^{-1}\) correspond to asymmetric and symmetrical stretching vibrations of the -CH\({}_{2}\)- group, which may belong to lipid chains or terminal protein groups [34; 35].  
The specific surface area and RH porosity were measured using the Gemini VI apparatus from Micromeritics. The BET area determined for rapeseed husks was 1064 m\({}^{2}\)/g, whereas the total pore volume determined for RH was 0.00223 cm\({}^{3}\)/g.
###### Abstract
Practitioners can choose relevant parameters and guidelines to suit their needs, thus accommodating site-specific and treatment considerations when evaluating drinking source water. The CCME WQI has been applied to characterize water quality for various purposes, including agriculture, the protection of aquatic life, and treated drinking water [19]. The importance and necessity of monitoring and assessing water quality using the WQI have been underscored by numerous experts around the world, as demonstrated in several studies [20; 21; 22]. In this study, we employed the Canadian Council of Ministers of the Environment Water Quality Index 1.0 (CCME WQI), a widely accepted and universally applicable computer model for assessing water quality. This index effectively highlights relative differences in water quality between sites, even when the same objectives and variables are used [23]. Comparison of the CCME WQI with other methods: The CCME-WQI method provides several benefits compared to other water quality index (WQI) methods. Firstly, it simplifies the analysis by reducing the number of variables needed for index calculation, which streamlines the process and shortens analysis time. Secondly, it offers a detailed assessment of water quality characteristics, delivering a more accurate representation of river conditions [24]. Additionally, the CCME-WQI method has been effectively employed to evaluate groundwater quality for drinking purposes, demonstrating its capability of offering a thorough assessment of water suitability for human use [25]. Furthermore, this method has also been used to monitor reservoir water quality over time, allowing for the examination of interannual and seasonal variations and the prediction of future water quality trends [26].
## 3 IoT Architectures
### IoT Architecture with FC
#### 3.3.3 Sensor or Physical Layer  
Specific sensors in the IoT's physical layer collect data from different locations which are then fed into the cloud infrastructure for decision-making. A large number of sensors are placed in a specific location, and a topology is developed for data transmission. In a standard network, there are sink nodes, sensor nodes, and control nodes.  
Fog nodes act as a bridge between the end device and the cloud infrastructure. The fog nodes are considered transient and consequently not trusted. The end device (for example, a user or an IoT smart device) must be authenticated by the fog node before any service is provided. This authentication can involve the cloud server, which introduces latency as well as overhead at the cloud server. Normally, authentication and session key establishment between an end device and the fog nodes relies on public-key cryptography. However, this is rather computationally expensive, relatively speaking. Fog nodes are considered transient possibly due to the nodes being out of range or becoming offline for various reasons. In this case, the end-user will need to re-authenticate with a secondary fog node that will take over the prior fog node's job. The primary goal of this research is to accomplish failover re-authentication without the need for public-key cryptography. This may be accomplished if the fog nodes agree on some security tokens ahead of time which are then made available to the end device after the first authentication. These security tokens may be used to provide quick authentication between the end device and the secondary fog node. Furthermore, unsecured communication between end devices and fog nodes can expose an adversary to a variety of security attacks such as replay, impersonation, man-in-the-middle, and denial of service attacks.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Development of Textile Materials
#### 3.3.2 Self-Cleaning Textile Materials  
Self-cleaning clothes will be demanded in future fashion due to their automatic washable capability. Attaching tiny metal structures with cotton fibres can break down dirt when exposed to sunlight. Researchers at RMIT University in Melbourne are working to develop a self-cleaning textile by integrating 3D copper and silver nanostructures woven into a piece of cotton fabric capable of degrading dirt, dust, and sweat when exposed to a concentrated light source. In their research, nanostructures absorb energy after exposure to light and create \"hot electrons\" to break down the dirt and clean the fabric [94]. A thin layer of titanium dioxide particles is used to coat the fabric [95]. When the sunlight falls on the semi-conductive layer, electrons are excited within the crystal structure and react with oxygen atoms in the air, which creates free-radical oxygen. The oxygen can break down compounds made by carbon using oxidation-reduction reactions. Dirt is then broken down into carbon dioxide and water (Figure 13).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 6 Emerging Trends and Future Directions
### Open Challenges and Limitations  
Despite these advancements, DVC still faces several challenges and limitations. One major challenge is the complexity of video data, which contains a vast amount of spatiotemporal information. Effectively extracting and utilizing this information remains a core challenge in video feature extraction. Moreover, the high-dimensional feature vectors of video data can lead to high computational complexity, necessitating efficient feature extraction, and dimensionality reduction techniques.  
Another limitation is the performance of event recognition and localization algorithms. While significant progress has been made, these algorithms still struggle with overlapping events, varying event durations, and subtle visual cues. Improving the accuracy and robustness of these algorithms is crucial for generating high-quality captions.
###### Abstract  
Satellite navigation spoofing is a major challenge in the field of satellite/inertial integrated navigation security. To effectively enhance the anti-spoofing capability of a low-cost GNSS/MEMS-SINS integrated navigation system, this paper proposes a method integrating a dual-antenna global navigation satellite system (GNSS) and a micro-inertial measurement unit (MIMU) to determine the two-dimensional directions of spoofing signal sources. The proposed method evaluates whether the single-difference carrier-phase measurements conform to the corresponding directions given in ephemeris files and employs micro-inertial navigation technology to determine the two-dimensional directions of the signal source. Based on a set of short-baseline dual-station measurements, the accuracy of the proposed method in determining the two-dimensional azimuths of satellites in synchronous orbits is verified, and the deviation from the real value is evaluated. The experimental results show that the proposed method can effectively identify the spoofed satellite signals while providing high-precision direction information at three different distances: 100 m, 10 km, and 36,000 km. The two-dimensional angle errors do not exceed 0.2 rad, 0.05 rad, and 0.01 rad, respectively.  
GNSS spoofing interference; MIMU; integrated navigation; spoofing signal source; integer ambiguity; common-clock receiver 2024164568[[https://doi.org/10.3390/rs16234568](https://doi.org/10.3390/rs16234568)]([https://doi.org/10.3390/rs16234568](https://doi.org/10.3390/rs16234568))
## 4 Experimental Results and Analysis  
### Cracking Pattern  
During the test, the cracks on the specimens were identified and hand-marked. Three types of cracks were observed in this experimental study, which include:  
* Diagonal cracks on two lateral faces at the center of the joint region (type 1).
* Diagonal (inclined) cracks outside of the joint region (type 2).
* Horizontal cracks located at the column faces (type 3).  
Figure 7 shows the crack patterns of specimen SC1 from the 0.5% drift to the 5.5% drift. From the 0.5% drift in the pull direction of loading, the first diagonal crack appeared at the center of the joint region. In the pull direction of loading (compression), some microcracks were also detected. They were  
Figure 5: Lateral loading protocol.  
Figure 6: Strain gauge (D and T) and rosette (R) locations.  
due to the local compression force resulting from the transmission of the shear force of the beam on the column. The cracks were propagated due to the 0.75% drift but they were particularly concentrated in the joint region. From the 1% drift, the microcracks formed in the pull direction had propagated. The diagonal crack appeared in two directions of loading but that damage did not take place outside of the joint region. The 1.5% drift provided the first damage that was observed outside of the joint region, including diagonal cracks (type 2) that extended from previous steps and some new horizontal cracks (type 3). The diagonal cracks propagated and widened gradually due to the 2.5% drift. However, the horizontal cracks (type 3) propagated slowly due to the presence of axial loading. Then, from the 3.5% drift, these horizontal cracks were connected to the diagonal cracks located outside of the joint region. In the last step of loading (drift of 5.5%), the diagonal cracks crossed the entire width of the specimen from the bottom corner to the opposite side at approximately 45 degrees.  
It should be noted that by adding the supplementary plates, the ordinal of failure modes of specimen SC2 was different in comparison with specimen SC1 (see Figure 8). From the 1% drift, the damage detected was the horizontal crack located outside of the joint region (type 3). Then, from the 1.5% drift, the diagonal cracks (type 1) appeared at the joint region but they propagated less in SC2 than SC1. From the 2.5%, 3.5%, and 4.5% drifts, the damage developed outside of the joint region in particular. They included the diagonal cracks that extended from the joint region (type 2) and various  
Figure 7: Crack pattern of specimen SC1.  
horizontal cracks crossing the half-width of the column. In the final step, the diagonal cracks on the front face connected to the vertical crack on the side face of the column. These cracks were opened, and consequently, the column was crushed. It was observed that the damage distributed to the inside and the outside of the joint region.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## Reasons for Software Defined Metro and Access Networks  
In this work the term'software-defined metro and access networks' refers to metro and access networks whose management is inspired to the principles of SDN, even if they are not fully compliant to the ONF standards and definitions. In our acceptation, software-defined metro and access networks have two main features, i.e., programmability and re-configurability (or flexibility). The programmability expected of software-defined metro and access networks fundamentally requires automation, abstraction of the underlying network resources, and intuitive high level service configuration. The re-configurability (or flexibility) of software-defined metro and access networks implies the capability of these networks to adapt dynamically to their operational situation in order to fulfill dynamical requirements of capacity, security, and quality of service with rapidity and efficiency. The main reasons for software-defined metro and access networks are sketched in Figure 1.