esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Stability Analysis of Multi-Temporal Calibration Results
#### 3.1.1. Ohs-F CMOS1 Calibration Results and Analysis  
Figure 9 shows the comprehensive gains of the CMOS1 sensor in 2020 and 2021. In 2020, the comprehensive gains at the wavelength range of 550 nm to 880 nm had high consistency, and the maximum RB was about 5%. For the bands outside the wavelength range, the RB of different comprehensive gains was more than 10%, especially for the band with the central wavelength of 896 nm. The comprehensive gains for these bands on 22 May, 3 June, and 26 June 2020 were quite different from those for the other two dates, indicating that the radiometric performance stability of these bands was low. In 2021, the consistency between the comprehensive gains at different dates was significantly better than that in 2020, and only the bands with central wavelength greater than 896 nm had relatively low stability. This shows that the radiometric performance of the CMOS1 sensor tended to be more stable in 2021.
## 3 Experimental Results and Discussions
### Failure Mode and Analysis
The shear stress between  
Figure 10: _Cont._  
Figure 9: Crack patterns and failure modes of: (**a**) S-0; (**b**) S-1; (**c**) S-2; (**d**) S-3. Equations (1) and (2) can be obtained according to the force equilibrium of the segmental model [36]. \[\Delta\sigma_{\rm w}A_{\rm w}-L_{\rm min}\overline{\tau}=A_{\rm r}f_{\rm r}/2 \tag{1}\]  
\[\Delta\sigma_{\rm s}A_{\rm s}+L_{\rm min}\overline{\tau}=A_{\rm t}f_{\rm t}/2 \tag{2}\]  
where \(A_{\rm s}\) and \(A_{\rm w}\) are the total areas of cross section of steel bar and steel wire, respectively; \(A_{\rm t}\) and \(A_{\rm r}\) are the areas of concrete and resin concrete in effective tensile zone, respectively; \(f_{\rm t}\) and \(f_{\rm r}\) are the tensile strengths of concrete and resin concrete, respectively; \(L_{\rm min}\) is the minimum spacing of cracks; and, \(\overline{\tau}\) is the average bond stress between SMRC and concrete. As shown in Figure 10b,c, the Equations (3) and (4) can be obtained according to the force equilibrium of the isolated bodies of steel bar and steel wire, respectively. \[\Delta\sigma_{\rm s}A_{\rm s}=\Omega_{\rm s}\overline{\tau_{\rm s}}L_{\rm min} \tag{3}\]  
\[\Delta\sigma_{\rm w}A_{\rm w}=\Omega_{\rm w}\overline{\tau_{\rm w}}L_{\rm min} \tag{4}\]  
where \(\Omega_{\rm s}\) and \(\Omega_{\rm w}\) are the total perimeters of steel bar and steel wire, respectively; \(\overline{\tau_{\rm s}}\) and \(\overline{\tau_{\rm w}}\) are the mean values of bonding stress between steel bar and concrete, and between steel wire and resin concrete, respectively. Based on Equations (1) to (4), the average spacing of the cracks (\(L_{\rm c}\)) can be expressed in Equation (5). \[L_{\rm c}=1.5L_{\rm min}=0.75(A_{\rm t}f_{\rm t}+A_{\rm r}f_{\rm r})/(\overline {\tau_{\rm s}}\Omega_{\rm s}+\overline{\tau_{\rm w}}\Omega_{\rm w}) \tag{5}\]  
Equation (5) shows that the average spacing of cracks decreases with the total perimeter of steel wires. The average crack width can be solved using the different strains in the steel wire and the resin concrete between two adjacent cracks. [PERSON] et al. [36] investigated the relationship between the crack width and the spacing between adjacent cracks that are based on the flexural structures reinforcement test. The crack width decreases with the decreasing of spacing between adjacent cracks.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 4 Discussion
### Geolocation
The variation in SHAP values is substantial, with some postal codes having a strong positive impact on prices and others a negative impact. This plot illustrates the granularity at which ZIP7 can affect property prices, likely reflecting very localized factors such as neighborhood desirability, school districts, or other community features. The SHAP values for these geospatial features exhibit positive and negative impacts, highlighting the model's ability to discern location-based pricing disparities. These results collectively underscore the importance of geospatial features in property valuation models, corroborating with the studies of [PERSON] (2021) [30], [PERSON] et al. [95], and [PERSON] et al. (2022) [32]. They also reveal how such features can capture broad regional trends and very localized market conditions mentioned by [PERSON] et al. (2023) [39], providing a multifaceted view of the real estate market. In terms of real estate market analysis, these insights can be instrumental. Market segmentation can be refined using these geospatial features, allowing for targeted marketing and development strategies. For investors, these data points can highlight potential areas of growth or decline. Urban planners may also use these insights to strategically allocate resources, identify conservation regions, or plan infrastructure to enhance property values. The SHAP analysis confirms the central role of location in determining real estate prices, with specific geographic coordinates and ZIP codes serving as proxies for many factors that influence desirability and value. Using tools like SHAP, machine learning models provide a powerful means to uncover these complex relationships, offering granular insights that can inform strategic decision making in the real estate market. However, for stakeholders in real estate, it is important to interpret these results cautiously, as correlation does not equate to causation, and overfitting remains a concern with highly granular features. Nonetheless, these results can be applied with a nuanced understanding of the market's complexity.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
###### Abstract  
Routing or forwarding information, such as the location of incidents and victims in a disaster, is significantly important for quick and accurate incident response. However, forwarding such information in disaster areas has been a challenging task for the Wireless Sensor Network as existing networks are affected (destroyed or overused) the disaster. Opportunistic information forwarding can play a vital role in such circumstances. Existing opportunistic routing protocols require huge message transmissions for cluster restoration, which is not energy efficient and results in packet loss. Hence, this paper introduces an energy efficient and reliable opportunistic density cluster-based routing protocol that opportunistically transmits data using a density-clustering protocol for emergency and disaster situations. Simulation results show that the proposed protocol outperforms some existing and well-known routing protocols in terms of network energy consumption, throughput and successful data transmissions.  
mobile sensor network; opportunistic routing; density clustering; disaster routing; energy efficient; throughput; packet loss ratio Article
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 3 Simulation Environment  
Simulations were perfomed at the Medium Access Control (MAC) Gen-2 packet level considering a realistic scenario for performance evaluation. Thus, we have added the proposed codec scheme in the already existing RFID Gen2 MATLAB simulator described in [18], and freely available for download at [21]. The modified MATLAB code is available as supplementary material.  
In the simulator, we assume that command packets (Select, Query, and ACKnowledgement) are generated by the reader and broadcast to the tags. Each tag responds individually to the command based on its current status (active/standby). Tags are implemented as independent state machines synchronized to the interrogator Query commands, so the number of tags active in each interrogation round is time-varying.  
We assume no error in the downlink, which means that the tags are always able to decode the reader packets. On the other hand, uplink tag packets are corrupted by AWGN channel noise and inter-tag interference; we do not consider inter-symbol interference (ISI), as usual for UHF RFID.  
The simulated reader is able to detect all tag collisions, as long as they transmit different RN16 numbers [16; 10].  
The simulator implements slow fading with Rayleigh distribution for both uplink and downlink channels, with fading coefficients \(\gamma_{T}\) and \(\gamma_{R}\) exponentially distributed--squared Rayleigh random variables--with unit mean.  
According to Gen-2 standards, tag symbols could be coded as either FM0 or Miller. The original implementation in the simulator was based on hard-decision Viterbi and Miller 4 for decoding tag packets. In contrast, our scheme consists of FM0 for coding tag symbols and the proposed codec algorithm. Figure 4 shows the MATLAB code which implements the encoding and decoding functions of the proposed scheme, as described in Section 2. Moreover, in order to enrich the comparison, we added the soft-decision version of the Viterbi decoding scheme as an alternative to the hard one. For both hard and soft decision decoding methods, we set \(k/n=1/2\), \(N=32\), \(K=16\), constraint length \(L=2\), and number of states \(S\) equal to \(2^{L}=4\).  
The setting of the simulation parameters is in line with Wireless Identification and Sensing Platform (WISP) [18], an actual implemented battery-free and programmable RFID sensor device  
Figure 4: Pseudocode implementing the proposed scheme: (**a**) encoding and (**b**) decoding functions for a block of length \(k\).  
compliant with the EPC Generation 1 protocol RFID platform [22]. The values of the main parameters used for simulations is shown in Table 1.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 2 Methodology
### Research Questions  
The paper aims to answer the following Research Questions:  
* RQ1: What are the main constraints and strategies when developing an EWS for disaster management and forecasting, especially when it comes to IoT-related aspects?
* RQ2: What are the most used IoT architectures and communication protocols in EWS for different types of disasters?* RQ3: How can existing EW systems be improved or optimized and what are the main gaps in literature and reviewed use cases? The questions were selected with the objective to both give a general overview of architectures and networks in the field of EWSs, and provide possible insights or solutions during the development of such systems, starting from its prerequisites and constraints up to the overall architecture. The goal of the reviewing process was to answer the RQs that were previously defined. For each article we highlighted the main objectives and the results, with the aim of showing both the progresses and the possible missing pieces or improvements that are needed in the more recent literature (RQ3). The reference architecture was used to analyze the papers and to easily extrapolate the main technologies used for each layer of the architecture developed in the various solutions (RQ2). Lastly, we provided some general recommendations to be able to satisfy the requirements of an EW system, based on the literature and the reviewed papers (RQ1).
## 2 Related Work
### Federated Learning
Experiments demonstrate that compared to traditional non-hopped systems, beam hopping systems provide higher capacity while reducingDC power consumption by approximately 50%, establishing a technical foundation for federated learning in Space-Air-Ground Integrated Networks. In UAV-assisted communications, [PERSON] et al. [26] investigated free space optical communication systems with an Amplify-and-Forward relaying strategy, analyzing the relationship between UAV positioning and optical beam width. Their findings revealed that system performance heavily depends on UAV position selection, offering valuable insights for device selection in federated learning across Space-Air-Ground Integrated Networks. These studies highlight the importance of considering dynamic network topology, heterogeneous communication capabilities, and bandwidth limitations when deploying federated learning in Space-Air-Ground Integrated Networks. The framework proposed in this paper builds upon these foundations to explore efficient privacy protection and knowledge sharing in complex network environments.
## 2 Materials and Methods
### Study Area
#### 2.1.1 Geological Setting  
According to field investigations and drilling, the stratigraphic units of the landslide area are divided into the alluvial layer and colluvium layer according to the genesis type [36; 37]. The landslide stratigraphy mainly consists of the Quaternary (Q) and Devonian (D), which are briefly described as follows: The Quaternary can be divided into alluvial and collapse accumulation layers according to the type of genesis. The alluvial layer is mainly distributed along the front edge of the landslide and composed of a gray-yellow sub-clay, the depth of the sub-clay on the left side is less than 2 m, and the depth on the right side is deeper, where it contains a small amount of gravel with a grain size of 2 mm to 10 mm, angular, and composed of strongly weathered muddy siltstone. The K195 + 770-K196 + 060 section of the landslide area is primarily a filled-in area, as, after natural accumulation, the soil slope formed via artificial crushing. The main component of the colluvium layer is gravel soil, with a gravel content of 50-80% and a thickness of 1 m-10.5 m. The composition is strongly weathered muddy siltstone and quartz sandstone, and the sandstone in some lots is seriously weathered. The soil-to-rock ratio is highly variable and irregular, with less gravel soil content in some lots. Nahkaoling formation  
Figure 1: Location of the Shangtan landslide.  
is mainly purple-red muddy siltstone, and the siltstone makes it difficult to determine the original rock structure. The lithology is more stable in the section from K195 + 100 to K195 + 600 where the outcrop is shallow, and the rest are buried deeper.  
Based on the above information and topographic data, a 3D mesh model was established, as shown in Figure 2. Defining the numerical model: the landslide mainly consists of clay soil (Sub clay), gravel soil (Gravel soil), and a siltstone layer (Rock), and the regions are defined as continuously distributed materials, and the constitutive model adopts the Mohr-Coulomb model, with the slope surface set as a free boundary and the bottom and surrounding of the model set as fixed boundary constraints. The initial ground stress field was generated using the elastic-plastic solution in stages.
## 3 Results
### Key Findings  
* Rainfall: The strong negative correlations for N-Total, P Tot, and COD (inflow and outflow) suggest that rainfall generally reduces the concentrations of these parameters due to dilution effects. The BOD5 level also shows a negative correlation in the inflow but a weak positive correlation in the outflow.
* Temperature: The temperature generally exhibits a negative correlation with most parameters, including the pH, N-Total, COD, and NH4 levels, indicating that higher temperatures tend to lower the concentration of these pollutants in both the inflow and outflow water. The P Tot in the outflow shows no significant effect of the temperature.
## 2 Materials and Methods  
### Preparation of the Au-SEBS Electrodes and Sensors  
All solvents were purchased from Sigma-Aldrich (Burlington, NJ, USA). The Au-SEBS electrodes were prepared according to a previous study [21]. First, 100 mm \(\times\) 100 mm glass plates (AMG glass, Uiwang, Korea) were prepared as substrates before gold deposition. The glass surfaces were cleaned for 2 min with oxygen plasma (100 W, 200 mTorr) using a reactive ion etcher machine (Oxford Plasmalab 80 Plus, Oxfordshire, UK) and sonicated in acetone, 2-propanol, and deionized (DI) water for 5 min each. After a gentle nitrogen gas blowing, the SEBS (TufteCT\({}^{\text{TM}}\) H1062, Asahi Kasei Co., Tokyo, Japan) solution was drop-cast on top of a glass plate (75 mg/mL in chloroform) and dried overnight. Next, gold nanomembranes with designated thicknesses (10, 30, 50, 70, and 90 nm) were directly deposited onto the surface of the SEBS films using the conventional thermal evaporation process (\(\sim\)1.0 A per second). After peeling off the Au-SEBS films from the glass plate, they were cut to specific sizes for each purpose (strain sensor, heater, and electrophysiology). The wavy and interdigitated sensor devices were prepared using the same process, but a stainless-steel shadow mask (iNEX JK Co., Ltd., Hwasengo, Korea) was additionally used for the temperature sensor and electrochemical sensor. The patterned shadow mask (approximately 80 mm \(\times\) 80 mm) was attached to the SEBS film on glass using commercial Kapton tape (KT-10, Bedell Co., Seoul, Korea) to create a sensor pattern. After the thermal evaporation of gold, the tape was peeled off, and the mask was removed from the surface of the SEBS film.
## 3 Results
### Effects of Treatments on Soil Chemical Properties under Wheat Agroecosystem
Regarding different soil layers, the soil NH\({}_{4}\)+-N content showed an obvious trend of first significantly (\(p<0.05\)) increasing in the surface soil layers and then decreased in the sub-surface soil layers, being the highest in 10-20 cm. Figure 4: Effect of tillage techniques and N fertilization methods on mineral N. Note: (**a**) is the concentration of NO\({}_{3}\)–N at different soil layers; (**b**) is the concentration of NH\({}_{4}\)+-N at different soil layers. The error bars indicate standard deviation. Different capital letters indicate significant differences between different treatments for the same soil layer (\(p<0.05\); [PERSON]’s test), and different lower-case letters indicate significant differences between different treatments and different soil depths (\(p<0.05\); [PERSON]’s test). CT: conventional tillage, CTN: conventional tillage with inorganic N fertilizer, CTM: conventional tillage with organic N fertilizer, CTNM: conventional tillage with combined application of organic and inorganic N fertilizer, NT: no-tillage, NTN: no-tillage with inorganic N fertilizer, NTM: no-tillage with organic N fertilizer, NTNM: no-tillage with combined application of organic and inorganic N fertilizer. The two factor ANOVA (Table 4) showed that the effects of tillage on SOC, TN, TP, NO3\({}^{-}\)-N, and NH4\({}^{+}\)-N were significant (\(p<0.01\)). The TN, NO3\({}^{-}\)-N, and NH4\({}^{+}\)-N contents were also significantly (\(p<0.01\)) affected by the N fertilization method. Tillage practices and N fertilization methods had significant effects on soil TN content, but the effects on the SOC, TP, NO3\({}^{-}\)-N, and NH4\({}^{+}\)-N contents were not significant (\(p<0.01\)). Response of Soil Stioichiometric Properties to Treatments under Wheat Mono-Cropping System Conditions  
Soil stoichiometric ratios under tillage systems and fertilization treatments are presented in Table 5. The soil stoichiometric ratios were significantly (\(p<0.05\)) affected by tillage techniques and N fertilization methods. The soil C/N ratio was the highest under CT treatment, and the lowest C/N ratio was recorded in NTNM treatment at the 0-10 cm soil layer. The NTNM treatment had, significantly (\(p<0.05\)), the highest soil C/P ratio, whilst CTM treatment had the lowest C/P ratio. The CT treatment had the lowest soil N/P ratio, whereas NTNM treatment had a maximum soil N/P ratio at the 0-10 cm soil layer.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Regional Regulations
#### 2.2.2 United States  
The Federal Communications Commission is the body in charge of the regulation of the radio electric space in the US [24]. For the 902-928 MHz ISM band, its use is limited to frequency hopping and digitally modulated radiators. US duty cycle regulation is more permissive, as well as the maximum TX power, than the European regulation.  
In the case of frequency hopping systems, devices have to be compliant with:  
* Channel hopping carrier frequencies should be separated by the greater between 25 kHz or the 20 dB bandwidth channel.
* If the 20 dB bandwidth of the hopping channel is <250 kHz, at least 50 hopping frequencies should be used and each up to 0.4 s per 20 s period.
* If the 20 dB bandwidth of the hopping channel is \(\geq\)250 kHz up to 500 kHz, at least 25 hopping frequencies should be used and each up to 0.4 s per 10 s period.  
For the case of systems using digital modulation techniques, the minimum 6 dB bandwidth should be at least 500 kHz.  
The maximum peak output power is:  
* If using channel hopping: 30 dBm if at least 50 hopping channels are used, 21 dBm if less than 50 hopping channels are used (minimum 25 hopping channels).
* If using digital modulation: 30 dBm. A duty cycle of 0.4 s each 20 s gives us a channel occupancy of 2% and in the best case, 0.4 s each 10 s, 4%.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 2 Related Work
### Edge Computing on IoT  
The continuous decline in sensor prices and computing costs has changed the architecture of traditional IoT with more \"things\" to be connected to the Internet, resulting in pushing computing to the edge of the network [11; 12]. As more networked devices become available, edge computing [13] is an important change required to make IoT systems more efficient and scalable, which will be used in all walks of life, especially in some areas where cloud computing is inefficient. Compared to cloud computing, edge computing shows the following advantages [14]. (1) A focus on real-time, short-cycle data analysis and better support big data analysis for cloud applications. (2) Real-time or faster data processing and analysis. Data processing is closer to data sources, rather than in an external data center of the cloud, so latency can be reduced. (3) Low cost. It costs less on data management solutions for local devices than on the cloud and data center networks. (4) Higher application runtime efficiency. As latency decreases, applications can run more efficiently at a faster rate. (5) Impairing the role of the cloud also reduces the likelihood of a single point of failure, and reducing the reliance on the cloud also means that some devices can run offline smoothly.  
From autopilot to smart agriculture, edge computing on IoT has been used in many areas. Companies like PointGrab and Gooee partner to provide IoT enabled lighting solutions with the help of real-time edge computing [15]. [PERSON] et al. present a fog-enabled embedded system for environmental monitoring [16]. Intel partner with AVOB to develop edge-enabled remote control and monitoring for IoT based smart energy management [17]. [PERSON] et al. propose an IoT architecture for connected vehicles and utilized fog computing as a platform for providing IoT services to connected vehicles [18]. [PERSON] et al. use cloudlets for big data analytics in a mobile cloud computing environment [19].  
The development of smart industry requires not only the high cloud but also the ubiquitous edge computing. Regardless of the efficiency of use of IoT applications, time delays or security considerations, edge computing is the key to the popularity of the IoT.
## 3 Crafting the Future: Unveiling 6G's Pinnacle Features, and
### Key Features of 6G
#### 3.2.5 Energy Efficiency Versus Spectral Efficiency  
The trade-off between spectral and energy efficiency is addressed by introducing energy harvesting technologies. User devices can harvest ambient energy, and environmental intelligence helps adapt to radio propagation environments, mitigating the spectrum-energy trade-off. In summary, 6G envisions a future with enhanced security, affordability, intelligence, energy efficiency, and bandwidth while carefully navigating trade-offs to deliver optimal user experiences [1].