esa-sceva/satcom-chunk-collection huggingface dataset:
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 1 Introduction
### Towards Battery-Less Communications  
What if wireless communications can work without any battery? We would not need any active power source. This proposal will allow us to avoid the trouble of charging, replacement, and recycling. Small devices in cellular communications will have everlasting energy. Battery-less communications  
Figure 1: Frequency band of mm-waves.  
also enable devices to be connected round the clock without any battery failure problems. The positive traits of battery-less communications include the following:  
1. Communication between devices can have a longer life.
2. Useful protocols of wireless communications could be long-lasting.
3. Devices will operate without power interruption.  
These traits would revolutionize the current concept of wireless communications. Moreover, these characteristics would enable battery-less D2D communications in disaster scenarios. Furthermore, we would achieve the goal of IoT to connect everything with emerging wireless networks more practically and without power interruption. Mobile phones would neither require a battery nor long-duration power charging for their operation.  
Previously it seemed unrealistic, but a recent research from the University of Washington introduced a prototype of a battery-free cellphone [8] that made battery-free communication more practicable. Figure 2 shows the block diagram of battery-free cellphone, which bypasses the power-thirsty components in battery-free cellphone design. The working principle of this phone depends on the reflection of radio waves, similar to methods used in radio frequency identification (RFID) systems [9]. Usually, RFID is made up of two parts: (i) transponder and (ii) reader. The transponder is placed in the product to be scanned, also known as a \"tag\". The reader is used for sending the RF signals and then extracting the data from reflected waves. Traditional RFID readers can work on either active tags, those having their power source, or on passive tags, which do not possess their energy supply. In passive tag communications, neither any oscillator nor any active component is needed to generate the carrier waves for the transmission of data. However, the tag only reflects incident carrier signals by varying the antenna impedance, after modulating its information to those incident signals. This type of communication is referred to as backscatter communications (BackCom) [10], which heralds battery-free communications. Recently, a multi-band development board is designed to support the battery-less autonomous semi-passive RFID transponder [11]. This design allows the devices to harvest energy from various electromagnetic fields (UMTS, LTE, and WiFi). The harvested energy can further be utilized by using an ultra-efficient power conditioner and storage block in a number of battery-free applications.
## 1 Introduction
We propose a new secure SDAA method that employs MMS (multiple mobile sinks) in the cluster-based network design (CBND). MMS, including ships, is used for appropriate vehicle path planning and avoids communication overhead. Comparatively, secure MAC protocols, such as SEFSC, SDA, and EEST, do not utilize MMS and CBND in their networks; therefore, the energy efficiency for the network is an issue. 2. The proposed SDAA protocol method minimizes latency/delay, providing reliability and ensuring energy efficiency provision in USNs (underwater sensor nodes) that improves packet delivery ratio, and reduces packet drop in the network, as compared to secure MAC protocols SEFSC, SDA, and EEST which only detects packet drops and delay, without accounting for any energy efficiency in the network. 3. We propose a new underwater vehicular sensor network (UVWSN) involving underwater vehicle (UV) deployment in the network that can detect and prevent all forms of malicious node attacks (MNA), for practical wireless sensor monitoring applications for the underwater vehicle (UV), as compared with secure MAC protocols, including SEFSC, SDA, and EEST, in which only UWSN was used to assess malicious attacks, without accounting for any UV. 4. Therefore, the proposed UVWSN ensures complete trustworthiness/privacy and integrity provision in the network, using trusted encryption-decryption schemes that improve security, compared to other secure MAC protocols, including SEFSC, SDA, and EEST, which only provide privacy without any data integrity provision in their network. The proposed SDAA protocol includes a cluster-based network design (CBND) that improves energy efficiency and prolongs the network lifetime of the USN for more extended application deployment. Secure MAC protocols, such as SEFSC, SDA, and EEST, use only authentication. However, it is not a cluster-based network (CBND); therefore, it is difficult to account for actual energy efficiency deployment in the UWSN. The remainder of the paper is as follows: Section 2 presents the related work of various secure MAC protocol classes; Section 3 presents design challenges of secure UWSN MAC protocols and models of secure data aggregation and authentication using UVWSN; Section 4 shows simulation setup and experimental results including discussion. Section 5 concludes the entire paper as follows.
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Bending Strength  
Figure 8 shows the bending strength of AFRP composites by ZnCl\({}_{2}\) and PDA treatment. In untreated conditions, the bending strength of the AFRP was 219.09 MPa and the best bending strength was obtained by P condition (247.85 MPa, increased by 13.13%). As a result of the single-treated conditions that were treated by S, P, 6Z and 10Z, the bending strength improvement trend for aramid fiber composites was P \(\geq\) S \(\geq\) Z. ZnCl\({}_{2}\) treatment obtained the lowest improvement in bending strength among the three treatments. Because ZnCl\({}_{2}\) treatment can improve bonding ability with resin by giving an etching effect to the aramid fiber surface, the performance improvement rate is reduced by lowering the performance of the fabric itself. Hybrid treatment conditions showed overall performance degradation. Among them, condition 6 ZP obtained lower bending strength than condition 6 PZ. Under the same hybrid treatment sequence condition, the 10 ZP condition with a high ZnCl\({}_{2}\) concentration can obtain higher bending strength than the 6 ZP condition. Therefore, in the hybrid condition, the bending strength is in the order of 6 PZ \(\geq\) 10 ZP \(\geq\) 6 ZP.  
Figure 8: Bending strength of 6-layer aramid fabric under various conditions.  
Figure 7: Impact energy of 6-layer aramid fabric under various conditions.
## 4 Results and Discussion
### Effect of Loading Conditions  
Considering that the stress field corresponding to the hydro-mechanical coupling stress function contains the related term of hydraulic pressure (Equation (9)), its effect on the calculation results of the SIFs and stress field needs to be investigated under different hydro-mechanical coupling loadings.  
The calculation model used is the same as that described in Section 4.1, and three different groups of hydro-mechanical coupling loading conditions are given (\(P_{h}\) = 15 MPa, \(\sigma_{y}\) = 0 MPa; \(P_{h}\) = 10 MPa, \(\sigma_{y}\) = 5 MPa; \(P_{h}\) = 5 MPa, \(\sigma_{y}\) = 10 MPa). Table 5 and Figure 6 provide the calculated SIFs and stress component (\(\sigma_{\theta}\)) for different loading conditions, respectively.  
It can be seen that for this calculation model, when the sum of \(P_{h}\) and \(\sigma_{y}\) is constant, the SIF calculation results are similar regardless of the single values of \(P_{h}\) and \(\sigma_{y}\). Furthermore, the same stress field can also be obtained by using the stress field definition of the common stress function [27] (Equation (26)) under different loading conditions.  
\[\begin{split}&\sigma_{\theta}=\frac{K_{1}}{\sqrt{2\pi r}}\cos^{3} \frac{\theta}{2}-\frac{3}{2}\frac{K_{2}}{\sqrt{2\pi r}}\sin\theta\cos\frac{ \theta}{2}\\ &\tau_{\theta}=\frac{1}{2}\frac{K_{1}}{\sqrt{2\pi r}}\sin\theta \cos\frac{\theta}{2}+\frac{1}{2}\frac{K_{2}}{\sqrt{2\pi r}}\cos\frac{\theta} {2}(3\cos\theta-1)\end{split} \tag{26}\]  
Obviously, the stress fields of the crack tip calculated when using Equation (26) are inconsistent with the actual situation; moreover, they do not satisfy the hydraulic loading conditions at the crack surface (\(\sigma_{\theta}\) is zero instead of \(P_{h}\)). On the contrary, when substituting the SIFs (Table 4) into the stress field defined by the hydro-mechanical coupling stress function (Equation (9)), it can be seen that there are significant differences in the magnitude of stress components under different loading conditions (Figure 6), and the boundary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(P_{h}\) = 15 MPa & \(P_{h}\) = 10 MPa & \(P_{h}\) = 5 MPa \\  & \(\sigma_{y}\) = 0 MPa & \(\sigma_{y}\) = 5 MPa & \(\sigma_{y}\) = 10 MPa \\ \hline Point A & 92.151 & 92.151 & 92.150 \\ Point B & 99.301 & 99.259 & 99.224 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hydro-mechanical coupling SIFs (\(K_{1}\)) for different loading conditions.  
Figure 6: The stress component (\(\sigma_{\theta}\)) for different loading conditions at \(r=b/6\).  
conditions can be satisfied. Therefore, more effective stress field results can be obtained by adopting our hydro-mechanical coupling stress function in the case of hydro-mechanical coupling loading.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
###### Abstract  
End-plate surface distance is important for length value dissemination in the field of metrology. For the measurement of distance of two surfaces, the positioning method is the key for realizing high precision. A practical method with nanometer positioning precision is introduced in consideration of the complexity of positioning laser sources of the traditional methods and new methods. The surface positioning is realized by the combination of laser interference and white light interference. In order to verify the method, a 0.1 mm height step is made, and an experiment system based on the method is established. The principle and the basic theory of the method are analyzed, and the measures to enhance the repeatability from optical and mechanical factors and signal processing methods are presented. The experimental result shows that the surface positioning repeatability is in the order of 10 nm. The measurement uncertainty evaluation shows that the standard uncertainty is 21 nm for a 0.1 mm step. It is concluded that the method is suitable to be applied to the length measurement standard of the lab.  
Keywords:white light interference; laser interference; surface positioning; end-plate surface distance measurement +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Measurement Setup: Requirements and Procedures
#### 2.2.1 Experimental Difficulties
For a different wavelength, the detuning may be either smaller or larger, thereby severely affecting the measured diffuse reflectance. During a wavelength sweep, one monochromator should therefore be set to a broader spectral bandwidth than the other while exceeding the spectral resolutions of each (e.g., \(\Delta\lambda_{\mathrm{Ex}}=8\) nm vs. \(\Delta\lambda_{\mathrm{Em}}=2\) nm). Naturally, the spectral bandwidth depends on the exact type and resolution of the utilized monochromators and the desired wavelength quantization in the experiment. It has to be optimized for each spectrometer separately. As with position-deviations, this effect is highlighted in more detail in Figure 11.
* Low detection signals: Noise becomes a sizable factor in measured signals. Because the diffuse reflectance is defined through a division between sample and white standard intensity, low count levels, especially with the white standard, will lead to potentially diverging diffuse reflectance values. This situation is mostly reinforced by considerable intensity differences of the excitation light source for varying wavelengths as well as by the utilized blazed gratings within the monochromators which are only designed for specific spectral ranges. With the present setup, this becomes apparent for wavelengths above 800 nm as will be shown in Figures 6 and 7. A remedy consists of splitting the investigated spectral range into subsections, optimizing each for satisfactory signal quality and finally merging all scans into a single diffuse reflectance spectrum. * Very high detection signals exceeding the detector's linearity range. For very large irradiances, the detector undergoes saturation, i.e., higher intensities lead to a sublinear increase of the signal, and thereby distort diffuse reflectance spectra (in the worst case, also damage the detector irreversibly). Subdivision of the spectral range and merging these individual scans may prove beneficial here as well. * Higher order diffraction from monochromators: Additional wavelengths unintentionally irradiate the sample and contribute to an artificially increased signal. Hence, additional wavelength discrimination by bandpass or longpass filters becomes mandatory. The resulting diffuse reflectance in the absence of such filtering will be shown in Figure 9b. * Material luminescence (fluorescence and phosphorescence): Additional light is emitted by the sample at a different wavelength. While spectral filtering of the emission signal provides some control about the wavelengths reaching the detector, a wavelength sweep from UV to VIS means that long-lived states may still luminesce when the emission wavelength is reached [57; 58]. Unless the substance to be characterized is known to be an upconversion material [59; 60], scanning from longer towards shorter wavelengths or at least limiting the investigated spectral range can help suppressing luminescence.
## 2 Performance Metrics
### Ambient Contrast Ratio  
Contrast ratio is a key display metric to enable a high dynamic range. Without any ambient light, the intrinsic contrast ratio of both the mLCD and OLED display panels could be higher than 10\({}^{6}\):1. However, in practical applications, the ambient light will degrade the contrast ratio due to the surface reflectivity of the display panel. A typical illuminance of a family room and an office room is about 100 lux and 300-500 lux, respectively. The ambient contrast ratio (ACR) is defined as follows [27]:  
\[\text{ACR}=\frac{L_{on}+L_{am}\times R_{L}}{L_{off}+L_{am}\times R_{L}}\, \tag{5}\]  
where \(L_{on}\) (\(L_{off}\)) represents the on (off)-state luminance of the display panel, \(L_{am}\) refers to the ambient luminance, and \(R_{L}\) is the ambient luminous reflectance of the display panel. Since the illumination conditions can greatly impact the reflectance and ACR, they should be explicitly defined. Figure 7a shows the top view of the measurement setting where two ceiling lights act as ambient light sources. The center of the ceiling light intersects with the center of the display panel at an azimuthal angle of 25\({}^{\circ}\) and a polar angle of 45\({}^{\circ}\). By measuring the brightness and ambient reflectance, the ACR as a function of ambient illuminance can be determined.  
We used a luminance meter (KONICA MINOLTA LS-110) to measure the brightness of the display panel. When the two ceiling lights were off, the full-screen white brightnesses of the mLCD and the OLED display were 997 nits and 431 nits, respectively. When the two ceiling lights were on, the ambient illuminance was 250 lux, as measured by the illuminance meter LX1330B. The ambient illuminance can be converted to ambient luminance by dividing a factor of \(\pi\). Therefore, the ambient luminance was 79.6 nits. When the panel was in the dark state and the two ceiling lights were on, the brightnesses of the mLCD and the OLED display were 2.3 nits and 1.1 nits, respectively. Therefore, the mLCD and the OLED display exhibited luminous reflectances of 2.89% and 1.39%, respectively. Such a low reflectance for the OLED display resulted from its excellent anti-reflection coating. Figure 7b shows that the mLCD still exhibited a slightly larger ACR than the OLED panel due to its higher peak brightness, even though its surface reflectivity was twice as high. If the mLCD adopted the same anti-reflection treatment (assume that the ambient reflectance was the same), its ACR would be -2.3 times higher than that of the OLED display at the ambient illuminance of 380 lux. Therefore, a high brightness has advantages under a high ambient lighting environment.  
Figure 7: (**a**) Top view of the ACR measurement setting. (**b**) Measured ACR of the mLCD and OLED panels.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
###### Abstract  
Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures.  
deep learning; machine learning; 6G networks; MIMO detection; MIMONet +
Footnote †: journal:
## 1 Introduction
### Objective and Contributions  
Systematic reviews allow us not only to answer clearly formulated questions, using systematic methods, but also to critically evaluate and synthesize results from multiple studies, thus consolidating knowledge and identifying gaps in a given research field.  
Concretely, the systematic review reported in this article aimed to identify relevant IT-based applications requiring smart cities' infrastructure to facilitate the mobility of older adults in the urban space and, consequently, their participation and inclusion in the community as full citizens.  
To the best of our knowledge, the systematic review literature reported by this article constitutes the first review that covers the mobility of older adults supported by smart cities' infrastructure. It aims to make the following contributions in terms of smart city applications to facilitate the mobility of older adults: (i) review of the main recently published research; (ii) identification and discussion of relevant applications; (iii) typification of the applications being developed; (iv) identification of current approaches to use sensors and smart city data to support the mobility of older adults; (v) analysis of the maturity level of the reported applications; (vi) discussion about the main results and contributions of the current research; (vii) identification of the barriers for the dissemination of the identified applications; and (viii) identification of research gaps. These contributions might be useful to inform smart cities' stakeholders about state-of-the-art solutions with impacts on active ageing and the gaps of the current research.
## 1 Introduction  
Mass industrialization of products in a factory environment requires product testing before exporting to the sales market. For example, end-of-line (EOL) testing contributes to validating the functionality of an antenna manufactured by Continental Advanced Antenna. In addition, the storage of information from the testing process allows the data to be manipulated by automated learning algorithms in search of a beneficial contribution. Studies in this area lead to the search and development of tools designed with objectives such as preventing production line anomalies, predictive maintenance, product quality assurance, demand forecasting, safety-problem prediction, resource augmentation, proactive maintenance, resource scalability, production time reduction, and anomaly detection, isolation, and correction. When applied to the manufacturing environment, these advantages allow the EOL system to be more productive, reliable, and time-saving.  
In this paper, we propose a tool that allows the visualization and early detection of trends associated with the antenna testing system. This tool focuses on the ability to predict failures that occur at the EOL by exploring the data provided from the tests, allowing the user to view graphs which show the progression of the data, as well as predict future data through an artificial intelligence model. The prototype was developed using the Visual Studio Code tool and Python programming language. Google Colab was chosen for model processing.
## 2 Related Works
### Research on Vehicle Clustering  
In recent years, many scholars have proposed vehicle clustering methods for vehicle communication application scenarios:  
[PERSON] et al. [18] proposed a multi-channel MAC protocol based on vehicle clusters and VANETs that could effectively guarantee QoS. This protocol uses different channels to transmit data within the cluster and outside the cluster, which greatly saves communication resources. In [19], [PERSON] et al. introduced vehicle clustering into VANETs and achieved good results. They also employed a stochastic cluster instability model to assess the rate of change in a cluster structure with cluster membership changes and cluster overlap states. In [20], [PERSON] et al. proposed a vehicle cluster method based on vehicle location, destination, and distance. Compared with the previous vehicle cluster algorithm, this method effectively reduces the resource occupation during vehicle communication. [PERSON] et al. [13] proposed a clustering method based on spatial sampling. This method ensures the stability of clusters and communication links. In [21], from the perspective of improving user experiences in high-speed mobile scenarios, [PERSON] et al. proposed a method to realize vehicle clustering using common OFDM parameters and clustering mechanisms. In this method, hybrid DSRC/WiMAX technology was utilized. The realized vehicular communication has achieved good results in various communication scenarios in the simulation experiments. In [22], [PERSON] et al. proposed a vehicle clustering protocol named MADCR, which first uses Euclidean distance to obtain vehicle clusters, and then uses the mayfly optimization algorithm to select the cluster head (CH). This method not only reduces the CH selection time but also effectively reduces the end-to-end delay and increases the transmission rate.  
In existing studies, clustering methods mostly rely on vehicle-to-vehicle negotiation, which increases the time overhead of clustering. This method, especially in highly delay-sensitive scenarios, such as CAVs, has difficulty meeting the requirements of a high response ratio and low delay. There is still a lack of efficient and fast clustering methods.  
Tabel \(1\) is the abbreviations used in this paper.