esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Sentinel-2 has become an attractive source for burned area monitoring because of its superior spectral properties, including the red-edge bands suited for chlorophyll content characterization [58], which provides the means to create novel indices for burn severity mapping [59]. Several previous studies have evaluated forest fires using the normalized burn ratio (NBR) with a high degree of success [47; 60]. The NBR exploits the near-infrared (NIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum [61]. This index is well-suited to detect fires in vegetation because the variations in the NIR usually represent the changes in the photosynthetically active vegetation, which is reduced by fire, whereas the modifications in the SWIR reflect moisture content [62; 63]. Indeed, fire results in a sharp contrast between the NIR and SWIR records [64]. Additionally, previous studies have used the NBR variants, of which the differenced normalized burn ratio (dNBR) is commonly applied [65]. The dNBR is a bi-temporal image differencing performed on pre- and post-fire NBR images [64; 66], to distinguish burn severity levels [65]. The fire category-based dNBR has become a practical reference to obtain burn severity information [67; 68]. In this study, we present a quick and affordable method for monitoring burned forest areas using Sentinel-2-derived NBR and dNBR indices within the GEE framework. Furthermore, we demonstrate the capacity of data from the recently launched Sentinel-5 satellite to track CO as one of the trace gases emitted during the burning of vegetation.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 3 Results
### Comparison of Inspection Data
#### 3.1.3 Comparison of Pre- and Post-Training Results  
To comprehensively evaluate the impact of training on the accuracy and repeatability of inspectors' measurements, this section compares the inspection data collected before and after the training. The analysis focuses on changes in key metrics, including the Mean Relative Error (MRE) and the Coefficient of Variation (CV). These comparisons provide a clearer understanding of the specific improvements in inspection performance achieved through the training program. The results of the comparison are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Inspector** & **First** & **Second** & **Third** & **Fourth** & **SD** & **CV (\%)** \\ \hline Inspector A & 15.8 & 43.2 & 15.6 & 13.7 & 1.16 & 7.7\% \\ Inspector B & 16.1 & 41.6 & 16.5 & 17.9 & 0.95 & 5.6\% \\ Inspector C & 15.1 & 16.3 & 43.5 & 15.1 & 0.69 & 4.5\% \\ Inspector D & 15.3 & 13.0 & 8.9 & 15.1 & 1.27 & 8.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Repeatability data of inspectors after validation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inspector** & **Measured Data** & **Average** & **Relative Error (\%)** \\ \hline Inspector A & 15.8 & 2.20\% \\ \cline{2-4}  & 15.6 & 0.91\% \\ \cline{2-4}  & 13.7 & 11.38\% \\ \hline Inspector B & 16.1 & 4.14\% \\ \cline{2-4}  & 16.5 & 6.73\% \\ \cline{2-4}  & 17.9 & 15.5 \\ \hline Inspector C & 15.1 & 2.33\% \\ \cline{2-4}  & 16.3 & 5.43\% \\ \cline{2-4}  & 15.1 & 2.33\% \\ \hline Inspector D & 15.3 & 1.03\% \\ \cline{2-4}  & 13.0 & 15.91\% \\ \cline{2-4}  & 15.1 & 2.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy data of inspectors after validation.  
The inspection data before and after training reveal a significant reduction in the Coefficient of Variation (CV) among inspectors following the training program (see Tables 3 and 5). Before training, Inspector D had a CV as high as 56.1%, which decreased to 8.8% after training, demonstrating remarkable improvement. Similarly, the CVs of other inspectors also decreased to varying extents, with an average reduction of 69.4%. These results indicate that after undergoing training, the stability and consistency of inspection data improved significantly, effectively reducing random fluctuations and individual discrepancies in measurements.
## 4 Discussion
### Drought Definition for Assessing SIF Variation during Drought  
We applied the scPDSI to define drought, which, in contrast to other drought indicators (e.g., CWD, SPI, or SPEI), considers soil characteristics and models water fluxes as runoff or recharge, along with common water balance drivers, such as precipitation, temperature, and evapotranspiration. Our drought definition excluded a considerable area of the western Amazon basin region which did not experience scPDSI \(\leq-2\) or P - PET \(<0\) conditions.  
When defining drought at a spatio-temporal scale comparable to that of OCO-2 SIF, the main source of uncertainty is the lack of long-term observational data on common water balance drivers [66] and the absence of in-depth high-resolution soil descriptors in UN-FAO maps [58]. Unless the Amazonian measurement station network is upgraded to provide the scientific community with better quality data, the responses of the Amazon rainforest's vegetation to climate change at an ER scale will have to be analyzed and forecasted using other climatological datasets (e.g., ERA-5 from the European Centre for Medium-Range Weather Forecasts [67]) and soil maps [68] in addition to those analyzed here.
## 3 Results and Discussion
### Profitability Assessment and Economic Impact
\begin{table}
\begin{tabular}{l r l} \hline \hline
**Input Parameters** & & \\ \hline Rated electric power & 125 & kW\({}_{\text{e}}\) \\ Available thermal power & 150 & kW\({}_{\text{th}}\) \\ Working hours & 3100 & h/year \\ Plant lifespan & 15 & years \\ Rate of biomass consumption & 1.28 & kg/kWh \\  & (496 & t/year) \\ Rate of biochar production & 15 & \% on input biomass, dry basis \\  & (74.4 & t/year) \\ Cost of the gasification plant (turnkey) & 2500 & \(\in\)/kW\({}_{\text{e}}\) \\ Cost of operation and maintenance & 0.03 & \(\in\)/kWh \\ Cost of exhausted olive pomace pellets & 30 & \(\in\)/t \\ Cost of labor (new part-time job) & 15,000 & \(\in\)/year \\ Cost of electricity & 0.095 & \(\in\)/kWh \\ Sale price of olive pits & 60 & \(\in\)/t \\ Sale price of biochar & 150 & \(\in\)/t \\ Non-refundable subsidies & 35 & \% of the total investment \\ Discount rate (real interest rate) & 1 & \% \\ \hline \hline
**Results (Considering Labor Costs of Hiring New Personnel)** & & \\ \hline Payback period & 8–9 & years \\ Net present value (NPV) & 111,161 & \(\in\) \\ Internal rate of return (IRR) & 7.26 & \% \\ Profitability index (PI) & 54.73 & \% \\ \hline \hline
**Results (without Considering Labor Costs of Hiring New Personnel)** & & \\ \hline Payback period & 5–6 & years \\ Net present value (NPV) & 319,137 & \(\in\) \\ Internal rate of return (IRR) & 16.72 & \% \\ Profitability index (PI) & 157.11 & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Input parameters and results of the economic feasibility assessment of the gasification plant. Table 10 shows that the payback period or return on investment is 8-9 years when deciding to hire new part-time maintenance personnel and the net present value (NPV) is roughly \(\copyright\)100,000. On the other hand, if the maintenance personnel already available in oil mills are trained on this type of technology, the payback period can be reduced down to 5-6 years. Under these circumstances, the investment would provide a net present benefit of around \(\copyright\)300,000 (NPV) during the plant lifespan and a profitability index (PI) higher than 1.5.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 1--Controlling Over Communication  
In this case, an attacker targets routing protocols and network traffic, and redirects the routing path from the original receiver to an insecure destination. Due to the high network dynamics and unpredictable nature of the IIoT systems, it is challenging to enforce a fixed and secure routing mechanism for the present IIoT systems. Therefore, with the lack of a predefined routing infrastructure, it is difficult to cooperate and communicate securely with one another in such systems. In a routing attack, an attacker can misconfigure routers, gateways, and even DNS (Domain Name System) servers and selectively drop messages unfavorably [73]. In a _DNS spoofing_ attack, an attacker takes control over the DNS server and unlawfully diverts network traffic to the attacker's devices that further launch attacks against the user's data confidentiality and network hosts [74]. Moreover, an attacker can spoof the identity of an attacked node's IP address and create a TCP (Transmission Control Protocol) session hijacking by impersonating a victim node. In such cases, attackers first capture an IIoT device and inject malicious codes into the router to impersonate a device's identity.  
The most common forms of this types of routing attacks are blackhole, wormhole, sybil, and pharming attacks [75]. In a blackhole attack, the compromised nodes divert data packets to another insecure location by falsely declaring a new direction to some targeted (i.e., malicious) destination and drops them before reaching the original destination. In a wormhole attack, an attacker makes a tunnel and secretly captures data packets from a specific location in the routing path and then transmits them to another insecure location. In a Sybil attack, an attacker creates a false identity (by providing false credentials) and uses this \"Sybil\" identity with another node to masquerade untrusted information to compromise the whole network [76]. In the case of a pharming attack, an attacker infects a large number of trusted nodes in a DNS server with malicious codes or changes the host files on a compromised computer. This redirects a legitimate website's data traffic to an insecure and fake site created by the attacker [77]. Returning to our use case of smart healthcare infrastructure, these are potential threats towards users' (i.e., patients) and devices' (i.e., the sensors attached to the patients) data privacy. In such cases, the attackers can masquerade a reputation system for gaining unauthorized control over the data traffic of the communications [78].  
Figure 5: Outline of the core security issues in an IIoT system.
# Development and Control of an Electro-Hydraulic Actuator System for an Exoskeleton Robot
## 5 The Position and Force Control Experiments
### Experiment Configuration and Control Experiments
In case of PID control, the error value has some positive offset in 0-1 s, 3-5 s, and 7-8 s intervals. This is because the applied cylinder is a single rod, not a double rod. In other words, because the volumes of both chambers are different, when the same amount of flow rate flows into the cylinder, a speed difference of the forward and backward movement of the rod occurs. In order to follow the sine wave, the motor must rotate at high speed in a section of 0-1, 3-5, 7-8 s. At this time, the limit of the PID controller was shown in this section. In terms of the \begin{table}
\begin{tabular}{c c c} \hline  & **Position Control** & **Force Control** \\ \hline SMC1 & \(K_{p}=200\), \(K_{s}=15\) & SMC1 & \(K_{p}=20\), \(K_{s}=15\) \\ SMC2 & \(K_{p}=100\), \(K_{s}=15\) & SMC2 & \(K_{p}=20\), \(K_{s}=1.5\) \\ PID & \(K_{p}=200\), \(K_{i}=0.02\), \(K_{d}=0.1\) & SMC3 & \(K_{p}=40\), \(K_{s}=1.5\) \\  & & PID1 & \(K_{p}=20\), \(K_{i}=0.001\), \(K_{d}=1\) \\  & & PID2 & \(K_{p}=30\), \(K_{i}=0.001\), \(K_{d}=1\) \\ \hline \end{tabular}
\end{table} 
Table 4: The parameters of the control for each experiment. Figure 12: Comparisons of the experimental results: (**a**) is the position control performance of the SMC (sliding motor control) and the PID control, (**b**) is a comparison of errors. performance of the controllers, SMC 1 was more accurate than SMC 2 in terms of following the desired input into the sliding surface. When the state variables enter the sliding surface, the control input \(\text{sgn}(s)\) is switched to the magnitude of \(K_{s}\). If the larger disturbance enters the system, it is temporarily outside of the surface boundary. Theoretically, a larger \(K_{s}\) value can be given to prevent this, but when applied to the actual system, a large vibration is generated. In the experiment, the average error of SMC 1 was 0.16 mm, which is a satisfactory value for a practical system. Figure 13 shows the results of the cylinder tracking the desired force through the sliding mode control and the PID control. The cylinder followed forces of 60, 90, and 120 N at 2 s intervals. As shown in Figure 13a, SMC 1 was set to \(K_{p}=20\) and \(K_{s}=15\), SMC 2 was set to \(K_{p}=20\) and \(K_{s}=1.5\), and SMC 3 was set to \(K_{p}=40\) and \(K_{s}=1.5\). Overshoot occurred in the system at \(K_{p}=40\), and the system became unstable. Also, chattering occurred when \(K_{s}=15\) even with the same \(K_{p}\) value. Therefore, the cylinder was most stable when \(K_{p}=20\) and \(K_{s}=1.5\) (SMC 2). Figure 13b shows a comparison between SMC 2 and the PID1,2 control. The PID control showed good follow-up but was unstable and less accurate than the designed SMC 2.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Detailed Design
#### 3.4.6 Data Layer  
In this layer, the developer should implement the data access logic that will later serve to encapsulate the data access mechanisms in the development of the business logic. The considered components are shown below.  
Within this layer, developers must implement their data access logic, which is crucial for encapsulating data access mechanisms when building the business logic. The following components are deemed essential for this layer:  
* Database Engine The proposed architecture aims to provide compatibility with various transactional database engines, allowing users to select the engine that best suits their requirements. These engines store and process information using objects such as tables, views, procedures, functions, and types, which can be utilized in business logic programming. The data access layer is responsible for optimizing the use of these resources, making them readily available in easy-to-use form for programmers. As depicted in Figure 8, the database engine's compatibility hinges on the support provided by the nest implementations. Connector classes are to be provided during the preparation stage, allowing the ORM library to effectively process database calls using a corresponding driver that aligns with the specified programming language. The interplay between the connectors and programming languages informs the selection of possible execution technologies, all derived from a single source code. While the RDBMS engines shown in the figure serve as examples to clarify the schema, it is worth noting that any database engine can be incorporated into the nest implementation without design constraints. It is crucial to acknowledge that developers might embed business logic directly into databases via stored procedures or custom functions. This can pose a challenge,as not every element can be effortlessly transferred between database engines. Consequently, it is advisable to confine the utilization of stored procedures to specialized tasks within the database, reducing the effort required during engine migration. As an alternative, ORM can support DDL, allowing it to generate these database logic objects directly in the respective languages of each database all from a singular transpiler implementation.
* Business Data Access In this layer, programming objects that correspond to the database objects are added, particularly for the representation of tables and views. The columns represent fields with data types translated into their programming equivalents. The relationships are interpreted as an array of related tables. These objects inherit base classes, have query methods and parameters for query operations, and are linked to the standard ORM query methods. This allows data from the transactional database to be represented in these objects, and their combination can lead to more complex queries.
# Towards a Real-Time Description of the Ionosphere:
## 7 Validation Results for _Inm_F2 Based on Ground-Based Ionosonde Observations
### Statistics on the Full Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ionosonde** & **Model** & **Res. Mean** & **RMSE** & **NRMSE** & **R** & **Counts** \\
**(Country)** & **Model** & **[km]** & **[km]** & **[km]** & **[\%]** & **R** & **Counts** \\ \hline Anyang and I-Cheon & IRI & \(-\)0.211 & 29.869 & 10.995 & 0.779 & 201,558 \\ (South Korea) & IRTAM & \(-\)1.389 & 30.911 & 11.379 & 0.794 & 201,558 \\ \hline Ascension Island (UK) & IRI & 0.826 & 34.775 & 12.186 & 0.638 & 264,916 \\ (UK) & IRTAM & \(-\)2.158 & 32.434 & 11.366 & 0.784 & 264,916 \\ \hline Athens (Greece) & IRI & \(-\)4.951 & 33.230 & 12.370 & 0.716 & 313,227 \\ (Greece) & IRTAM & \(-\)6.015 & 26.230 & 9.765 & 0.851 & 313,227 \\ \hline Boa Vista (Cape Verde) & IRI & \(-\)6.362 & 35.615 & 11.520 & 0.736 & 60,295 \\ (Cape Verde) & IRTAM & 3.007 & 26.703 & 8.637 & 0.852 & 60,295 \\ \hline Boulder (USA) & IRI & 4.083 & 29.890 & 11.126 & 0.763 & 420,504 \\ (USA) & IRTAM & \(-\)3.033 & 25.579 & 9.522 & 0.836 & 420,504 \\ \hline Cachoeira Paulista (Brazil) & IRI & 16.579 & 45.903 & 15.790 & 0.499 & 158,881 \\ (Brazil) & IRTAM & 8.994 & 32.261 & 11.097 & 0.789 & 158,881 \\ \hline Chilton (U.K.) & IRI & \(-\)3.192 & 29.583 & 10.891 & 0.831 & 269,815 \\ (U.K.) & IRTAM & \(-\)7.850 & 28.018 & 10.315 & 0.861 & 269,815 \\ \hline Dourbes (Belgium) & IRI & 0.887 & 25.512 & 9.526 & 0.845 & 328,740 \\ (Belgium) & IRTAM & \(-\)1.512 & 21.726 & 8.112 & 0.889 & 328,740 \\ \hline Dyes AFB (USA) & IRI & 3.456 & 36.564 & 13.355 & 0.708 & 142,345 \\ (USA) & IRTAM & \(-\)5.363 & 32.746 & 11.961 & 0.785 & 142,345 \\ \hline Eielson (USA) & IRI & \(-\)15.912 & 28.692 & 11.911 & 0.759 & 54,960 \\ (USA) & IRTAM & \(-\)8.381 & 24.055 & 9.986 & 0.817 & 54,960 \\ \hline El Arenosillo (Spain) & IRI & 11.753 & 30.989 & 10.907 & 0.792 & 206,362 \\ (Spain) & IRTAM & 7.118 & 24.843 & 8.744 & 0.866 & 206,362 \\ \hline Fortaleza (Brazil) & IRI & 3.462 & 41.126 & 13.206 & 0.725 & 115,951 \\ (Brazil) & IRTAM & 9.718 & 34.957 & 11.225 & 0.832 & 115,951 \\ \hline Gakona (USA) & IRI & \(-\)12.830 & 36.554 & 14.376 & 0.729 & 188,948 \\ (USA) & IRTAM & \(-\)15.801 & 35.878 & 14.110 & 0.784 & 188,948 \\ \hline Goose Bay (Canada) & IRI & 5.420 & 36.757 & 13.826 & 0.792 & 138,063 \\ (Canada) & IRTAM & 3.144 & 34.465 & 12.964 & 0.801 & 138,063 \\ \hline Grahamstown (South Africa) & IRI & 2.539 & 25.744 & 9.616 & 0.764 & 389,105 \\ (South Africa) & IRTAM & \(-\)2.788 & 23.069 & 8.617 & 0.825 & 389,105 \\ \hline Guam (USA) & IRI & \(-\)15.517 & 36.903 & 12.338 & 0.700 & 150,696 \\ (USA) & IRTAM & \(-\)2.786 & 27.130 & 9.071 & 0.860 & 150,696 \\ \hline Hermanus (South Africa) & IRI & 3.261 & 21.424 & 8.029 & 0.827 & 297,534 \\ (South Africa) & IRTAM & \(-\)6.104 & 20.670 & 7.746 & 0.862 & 297,534 \\ \hline \end{tabular}
\end{table} 
Table 6: Same as Table 5, but for each of the _hm_F2 datasets relative to the 40 ionosonde stations listed in Table 1. The number of counts on which the statistics were calculated is reported in the rightmost column.
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 2 Literature Review
### Traffic Information Estimation Methods
#### 2.2.2 Signal Statistics  
Some studies analyzed the relationship between traffic information and the amount of cellular network signals (e.g., NLU, PLU, HO and CA) on the road segments and proposed traffic information estimation methods based on signal statistics. For instance, [PERSON] _et al._[22] collected and counted the handover events, which were triggered when the communicating MSs entered a new cell for traffic flow estimation. Furthermore, a virtual traffic counter at inter-cell boundaries was proposed to monitor call events in different cells and to estimate traffic flow [23]. A method was proposed to retrieve the NLU events, which were triggered when MSs entered a new location area, and to estimate traffic flow in accordance with the amount of NLU events [24]. For traffic density estimation, a method was proposed to monitor the CA events and to analyze the relationship between traffic density and the amount of CA events [25]. These studies indicated that their proposed methods can estimate traffic information precisely with anonymous MSs.
## 3 Results and Discussion  
### Preliminary Test for Acetaldehyde Removal  
Before modifying the surface functional group with amine species over the adsorbents, preliminary tests are performed to compare the CACF with other commercials (Supplementary Data 1a). The breakthroughs for the conversion of acetaldehyde removal under the given conditions are in the following order: CACF (\(\approx\)0.4 h) \(>\) K-C (\(\approx\)0.15 h) \(>\) MSP-20 (\(\approx\)0.05 h) \(>\) QW (\(\approx\)0 h). In general, oxygen content is released as CO and CO\({}_{2}\) from carboxyl groups conjugated with ketone or quinone, carboxyl groups in non-aromatic or aromatic compounds, carboxyl anhydrides, and lactone structures [29; 30; 31]. Thus, better performance of acetaldehyde over CACF might be attributed to the relative abundance of oxygen content on the surface.  
To introduce the amine functional group, CACF calcification was performed at various temperatures. The CACF that was calcined at 1100 \({}^{\circ}\)C (CACF-H) showed an optimized acetaldehyde removal performance (Supplementary Data 1b). In previous reports [29; 30; 31], the temperature of calcination was adequate for the heat treatment of ACF, being reasoned with the crystallinity and hydrophobicity. However, when CACF was calcined at 1400 \({}^{\circ}\)C its removal performance decreased, possibly due to the partial destruction of micropores and a rearrangement of the carbon structure into a graphitic structure.