esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### AI in the Assessment and Management of Ecosystem Services
The study in [47] employed the Google Earth Engine cloud platform and machine learning algorithms to map coastal green infrastructure in Puducherry, India. By combining Sentinel-2 satellite imagery and ALTM laser topography, a Random Forest model was developed to effectively monitor ecosystems that provide essential services, including flood protection, carbon sequestration, and biodiversity conservation. In [115], AI was used to analyze soil resource changes and assess the carbon storage potential of ecosystem services in an Indian watershed using the InVEST model. Land-use mapping and carbon stock estimation were based on remote sensing data and ground control points processed through machine learning algorithms. The model enabled a quantitative assessment of current ecosystem service conditions and simulated two land-use change scenarios: forest expansion and agricultural land expansion. The results highlight the potential of machine learning and spatial modeling for evaluating ecosystem functions. Overall, the analysis of these studies demonstrates the growing significance of AI in the quantitative assessment and spatiotemporal modeling of ecosystem services across diverse socio-ecological contexts. AI tools enable the integration of remote sensing data, field observations, and spatial and socio-economic information, facilitating a comprehensive approach to evaluating natural capital. These tools contribute to the development of adaptive management strategies that consider local ecosystem characteristics, anthropogenic pressures, climate risks, and social vulnerability. Thus, the reviewed research underscores AI's emerging role as a key enabler of local ecosystem resilience and the continued provision of essential ecosystem services under global environmental change. In total, according to the regional classification, the following references pertain to Asia [47; 51; 65; 97; 98; 99; 100; 101; 102; 103; 109; 111; 115]; Africa [10; 104; 112]; Europe [107] and Latin America [106; 113].
# Wireless Positioning in IoT: A Look at Current and Future Trends
###### Abstract  
Connectivity solutions for the Internet of Things (IoT) aim to support the needs imposed by several applications or use cases across multiple sectors, such as logistics, agriculture, asset management, or smart lighting. Each of these applications has its own challenges to solve, such as dealing with large or massive networks, low and ultra-low latency requirements, long battery life requirements (i.e., more than ten years operation on battery), continuously monitoring of the location of certain nodes, security, and authentication. Hence, a part of picking a connectivity solution for a certain application depends on how well its features solve the specific needs of the end application. One key feature that we see as a need for future IoT networks is the ability to provide location-based information for large-scale IoT applications. The goal of this paper is to highlight the importance of positioning features for IoT applications and to provide means of comparing and evaluating different connectivity protocols in terms of their positioning capabilities. Our compact and unified analysis ends with several case studies, both simulation-based and measurement-based, which show that high positioning accuracy on low-cost low-power devices is feasible if one designs the system properly.  
Internet of Things (IoT); wireless positioning; indoor location +
Footnote †: journal: Sensors
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 3 Biological Neurons: Estimating Parameters from Guessed Models
### Supplementing Gradient Descent with Statistical Inference  
Current clamp recordings of the membrane voltage, \(V_{data}(t)\), may be written as the sum of the useful signal, \(V_{use}(t)\), and a small error component, \(v_{n}(t)\), accounting for noise and model error. The cost function:  
\[C\left(\stackrel{{\rightarrow}}{{x}}(0),\stackrel{{ \rightarrow}}{{p}}\right)=\frac{1}{2}\sum_{i=0}^{i=N}\left(V_{data}(t_{i})-V \left(t_{i},\stackrel{{\rightarrow}}{{x}}(0),\stackrel{{ \rightarrow}}{{p}}\right)\right)^{2} \tag{5}\]  
defines the energy surface whose minimum we seek. This total energy may be decomposed as the sum of a free energy, \(F\), and a random energy, \(TS\) as [8]:  
\[C\left(\stackrel{{\rightarrow}}{{x}}(0),\stackrel{{ \rightarrow}}{{p}}\right)=F\left(\stackrel{{\rightarrow}}{{x}}(0 ),\stackrel{{\rightarrow}}{{p}}\right)+TS \tag{6}\]  
where  
\[F=\frac{1}{2}\sum_{i=0}^{i=N}\left(V_{use}(t)-V(t_{i},x(0),p)\right)^{2} \tag{7}\]  
and  
\[S=2(N+1)\ R\ \Delta f \tag{8}\]  
Minimizing the cost function by a deterministic process such as gradient descent only minimizes the free energy part. The misfit error \(\delta c=TS\) can only be minimized by statistical inference. Since we have assumed small experimental error, the misfit error determines the surface of an ellipsoid centered on the optimal parameter solution \(\stackrel{{\rightarrow}}{{p}}^{*}\). Thisellipsoid is given by the second order term in the Taylor expansion of the cost function, where \(\hat{H}\) is the Hessian at the site of \(\overrightarrow{p}^{*}\).  
\[\delta C=\frac{1}{2}\Big{(}\overrightarrow{p}-\overrightarrow{p}^{*}\Big{)}^ {T}\hat{H}\Big{(}\overrightarrow{p}-\overrightarrow{p}^{*}\Big{)} \tag{9}\]  
Starting the search of parameters from different initial guesses will generate parameter solutions, \(\overrightarrow{p}_{k}\), distributed across this ellipsoid surface (Figure 4a). The true solution may therefore be approached by taking the mean value of the statistical sample of solutions derived from \(K\) initial guesses.  
\[\overrightarrow{p}^{*}\approx\ \frac{1}{K}\sum_{k=1}^{K}\overrightarrow{p}_{k} \tag{10}\]  
The approach has been successful in estimating the parameters of interneurons from the songbird high vocal center [8]. Figure 4b shows the activation and inactivation thresholds obtained by averaging the parameter solutions emanating from 84 initial guesses. These are the gate thresholds of all ion channels expressed in two interneurons, N1 and N2. The variance (vertical bars) indicates the dispersion of the underlying \(p_{k}\) values. The average thresholds (dots/square symbols) otherwise become remarkably consistent across the two interneurons [8]. This consistency strongly suggests that averaging the thresholds obtained by gradient descent gives sensible estimates of the true biological thresholds. Similar consistency was obtained across ionic conductances and recovery time constants. The variance of the latter was, however, greater.
## 3 Channel Coding Technology
In polarization channel information bit selection, the error probability of each subchannel can be calculated by a large number of simulations, according to the bit error rate of the subchannel in order from smallest to largest. For the code length D, with code rate D/K polarization coding, the first K channels transmit information bits, and D-K subchannels place frozen bits. The Monte Carlo construction method requires a complete process of Polar coding and decoding. The difference is that, in the Monte Carlo construction method, the step of selecting information bits is placed last and, at the beginning, all subchannels place frozen bits. Decoding adopts relatively simple but more effective successive elimination decoding (SC decoding), which is slightly different from the decoding step; in the Monte Carlo construction method, only the bits that currently need to be decoded are unknown and the others can be used as known bits, so that a series of errors will not occur because of a bit decoding error. Next, the calculated logarithmic ratio of likelihood is decoded by hard judgment and compared with the frozen bit to calculate its error probability. As the encoding matrix and decoding are independent of the channel, the Monte Carlo construction method can be applied to the selection of any channel information bit. Assuming the code length is D, the channel parameters of the polarizer subchannel can be used, as shown in Equation (12). \[ \begin{split}\text{Z}(W_{D}^{(i)})&=\sum\limits_{y _{1}^{N}\in y^{N}}\sum\limits_{u_{1}^{i-1}\in x^{i-1}}\sqrt{W_{D}^{(i)}(y_{1}^ {N},u_{1}^{i-1}|0)W_{D}^{(i)}(y_{1}^{N},u_{1}^{i-1}|1)}\\ &=E\Bigg{\{}\sqrt{\frac{W_{D}^{(i)}(Y_{1}^{N},u_{1}^{i-1}|u_{1} \oplus 1)}{W_{D}^{(i)}(Y_{1}^{N},u_{1}^{i-1}|U_{i})}}\Bigg{\}}\end{split}  \tag{12}\]  
where \((U_{1}^{D},Y_{1}^{D})\) is the sampling of the joint probability distribution \(P_{U_{1}^{N},Y_{1}^{N}}(u_{1}^{D},y_{1}^{D})\triangleq 2^{-D}W_{N}(y_{1}^{D} \big{|}u_{1}^{D})\). In the Monte Carlo method, the corresponding mean is \(\hat{Z}(W_{D}^{(i)})\). The steps to construct the Polar code are as follows:  
(1) Generate a 0-bit sequence \(u_{A}\) with length D and multiply the generation matrix \(G_{D}\) to obtain the polarization code \(x_{D}\);  
(2) Send \(x_{N}\) into the channel to obtain the receiving codeword \(y_{D}\) at the receiving end;  
(3) Use Equation (12) to calculate the likelihood ratio in the initial channel for each bit in \(y_{D}\);  
(4) Use SC decoding to calculate the sending information of each subchannel;  
(5) Count the bit error rate of each subchannel, then select K subchannels with a low bit error rate for transmitting information bits, and use the remaining D-K channels to transmit freezing bits.
## 3 Results and Discussion
### Isothermal Analysis
#### 3.3.2 Langmuir Isotherm  
When the Langmuir isotherm was applied, the obtained results agreed with the data throughout the experiment, with a high correlation coefficient of R\({}^{2}\) = 0.993 for the MWCNTs. Furthermore, the IV2R dye showed a high maximal uptake capacity (q\({}_{\text{max}}\)) of 76.92 mg g\({}^{-1}\) by the MWCNTs (Figure 12). This is consistent with the formation of a complete monolayer on the surface of the adsorbent. The Langmuir constant (b), which is related to the heat of adsorption, was found to be 1.42.  
Figure 11: Freundlich isotherm of the IV2R onto the MWCNTs.  
Figure 12: Linear Langmuir isotherm for the sorption of IV2R.  
3.3.3. Halsey and Henderson Isotherm  
Halsey and Henderson isotherm equations are suitable for multilayer adsorption; in particular, the fitting of these equations can be heteroporous solids [60]. A plot of \(\ln q_{e}\) versus \(\ln C_{e}\) Halsey and \(\ln[-\ln(1-C_{e})]\) versus \(\ln q_{e}\). [PERSON] and Henderson adsorption isotherms are given in Figures 13 and 14, respectively. The isotherm constants and correlation coefficients are summarized in Table 2. [PERSON] showed a correlation coefficient of R\({}^{2}\) = 0.989, while [PERSON] showed an R\({}^{2}\) of 0.988. The results obtained for [PERSON] and [PERSON] show that both models are applicable for the adsorption of IV2R onto MWCNTs.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.4 Telecommunication Subsystem (COMM)  
IDEASSat used S-Band and UHF as dual-frequency satellite communications bands. The high-bandwidth S-Band transmitter was used for the downlink of scientific data and bulk replay of saved flight data. The UHF transceiver used the 70 cm amateur radio band for command uplink and beacon broadcasting. The beacon packets contained flight and tracking data, and were routinely transmitted by the spacecraft at 30 or 60 s intervals,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subsystem** & **Input Voltage (V)** & **Power (W)** \\ \hline ADCS & 12 & 6.74 \\ GPS Receiver & 3.3 & 1.8 \\ CDH & 3.3 & 1.4 \\ UHF Tx/Rx & 6 & 10/0.2 \\ S-Band Tx/Stand-by & Battery Voltage & 5/0.6 \\ CIP & 12 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power consumption of IDEASSat subsystems.  
depending upon operational mode. Beacon packets were also saved by CDH to the two onboard SD cards at the same time they were transmitted for potential future replay.  
IDEASSat used the STX-C CubeSat S-Band transmitter module developed by the Cape Peninsula University of Technology (CPUT) for high bandwidth scientific and flight data transmission. According to \"State-of-the-Art Small Spacecraft Technology [12]\", the technology readiness level (TRL) of this module had reached 7+. For the detailed configuration and related description of STX-C please refer to the description in [2]. S-band data packets were formatted according to Consultative Committee for Space Data Systems (CCSDS) specifications and encoded using half rate convolutional encoding for error correction.  
The SpaceQuest TRX-U transceiver module was used to realize the execution of command uplink and beacon broadcasting using the UHF amateur radio frequency band. The antenna design was based on the monopole antenna made with spring steel tape in the MinXSS mission [11]. IDEASSat had three beacon formats as shown in Table 3.  
In the preliminary design, the packet format was similar to AX.25 and contained the housekeeping data of each subsystem. Cyclic redundancy check (CRC) 16 checksums were included for error detection. However due to lack of prior experience with spaceborne packet radio, the preamble specified in the AX.25 structure was not implemented [13]. The purpose of the preamble is to allow for more reliable signal acquisition. During testing, as a result it was found that the packet error rate (PER) of the original (normal) packet was unacceptably high. Since the PER is proportionate to packet size, an unconventional quick fix solution was implemented whereby the size of the beacon packet was reduced. Through end-to-end communication tests, it was found that arranging the original packet into nine smaller packets for transmission (lite packet type) resulted in a lower PER. This also proved that the life format of multiple headers was helpful for the detection of packets at the reception end.
# On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model
## 3 Extending the MAPE Scheme: The DSR  
### The Deep State Representation  
The Deep State Representation (DSR) is a multi-labelled directed graph that holds symbolic and geometric information within the same structure. Figure 1 shows one simplified example. The **robot** and the **roll container** are geometrical entities, linked to the **world** node (a specific anchor providing the origin of coordinates) by a rigid transformation. At the same time that we can compute the metric relationship between **robot** and **roll container** (\(RT^{-1}\times RT^{\prime\prime}\)), this **roll container** can be located close to the **robot**, and hence, the robot can launch the procedure for picking it up. In parallel, an agent can annotate that **people** are not_detected close to the **robot**. Features, such as the level of the **battery**, are annotated as properties of the specific node linked to the **robot**. Context features, or more elaborated perceptions obtained from them, are updated at runtime.  
This simple example shows that all agents in the software architecture (navigation, person-related perception...) use the DSR for sharing information about specific parts of the context, creating a whole view of the current state. Furthermore, it also stores a complete view of the activities being performed by the robot, joining perceptions and actions within a shared structure. Details are annotated in nodes and arcs. For instance, a battery node has an internal level value, which will be updated by a specific agent in the software architecture.  
In addition to the update of the DSR, the agents in the software architecture will read the DSR to search for those changes that trigger their specific task-solving skills. This mapping between changes in the DSR and actions is encoded within each agent. For instance, when a goal pose is set, the Navigation agent traces a route and moves the robot to this pose. In the internal grammar of each agent, rules are encoded as triplets with the states of the DSR after, during, and before the agent executes a specific action. For more details about the DSR, please see the work by [5; 6]. Next, we extend the idea of using the DSR as a runtime model or as a mechanism for synchronizing running MAPE loops.  
Figure 1: Unified representation as a multi-labelled directed graph. For instance, edges labelled as close or is not denote logic predicates between nodes. On the other hand, edges starting at **world** and ending at **robot** and **roll container** are geometric and they encode a rigid transformation (\(RT\) and \(RT^{\prime\prime}\) respectively) between them. Geometric transformations can be chained or inverted to compute changes in coordinate systems (see text).
## 2 Full Recycled Support Technology of Steel-Pipe-Anchored Sheet Pile  
The full recycled support system of steel-pipe-anchored sheet pile consists of various components, including steel sheet piles, steel pipe piles, steel tie rods, and steel waist beams. As depicted in Figure 1, all these elements can be retrieved after the pit is backfilled.  
The sheet piles are composed of cap sections with locking openings, which facilitate their interconnection to form a continuous and tightly integrated sheet pile wall (Figure 2a). The steel pipe piles serve as anchor members, consisting of large-diameter circular steel pipes (Figure 2b). Steel tie rods function as the connecting elements between the retaining member (steel sheet pile) and the anchor ingot member (steel pipe pile). These tie rods are configured on a one-to-one basis with the steel pipe piles, utilizing thick-walled steel pipes or hollow anchor rods (Figure 2c). Threaded anchor heads are positioned at both ends of the tie rods, which are then secured to the steel sheet pile and steel pipe pile using nuts, respectively. The steel girdle beam serves as the force-transmitting component between the steel tie rod and the steel sheet pile. It facilitates the uniform distribution of force across the steel sheet pile and is constructed from two-piece H-shaped steel (Figure 2d).  
The construction and recycling procedure follows the following steps: first of all, drive the sheet piles and steel pipe piles. Then, excavate the foundation pit to 1 m from the top of the excavation. Place the anchor rod and tighten both ends with an internal and external anchor. Finally, install the wake beam. When backfilling the foundation pit, the recycling procedure follows the previous steps in reverse to complete the operation.  
Figure 1: The full recycled support system of steel-pipe-anchored sheet pile.
## 2 Materials and Methods
### Wastewater Treatment by Microalgae Cultivation  
Batch cultivation of microalgae was conducted using a mixture of SW and DFW to evaluate microalgal growth, nutrients removal and algal lipid production. Different mixture ratios of the two types of wastewaters were tested. Microalgae seeds for wastewater treatment were prepared in 500 mL flasks using BG-11 medium at 25 \({}^{\circ}\)C with a light intensity of 100 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). The pretreated FW was first diluted 10 times with sterile tap water to decrease its high concentration of COD. Subsequently, the DFW was further mixed with the SW to prepare the wastewater mixture for microalgae cultivation. The different mixture ratios (SW:DFW, \(\sigma/v\)) were 1:0.5, 1:3, 1:9 and 1:19. The mixed wastewater was autoclaved at 121 \({}^{\circ}\)C for 20 min after the pH was adjusted to 7.1. When the temperature of mixed wastewater was cooled to room temperature, the inoculum was then inoculated into 500 mL flasks containing 200 mL of mixed wastewater with a ratio of inoculum to mixed wastewater 1:10 (\(v/v\)). The flasks were placed in a shaker with a rotation speed of 150 rpm to cultivate microalgae for 8 days. Culture temperature and light intensity were controlled at 25 \({}^{\circ}\)C and 200 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). All of the experiments were conducted in triplicate. During the culture process, microalgal biomass, COD, TN and TP were measured daily, while the lipid content of microalgae was measured at the beginning and end of the culture process.
## 1 Introduction
Different PSC systems may clearly result in the same soil texture being accounted to different ways for the relative different composition of soil sand, silt, and clay contents, and thus having potential effects on the calculation of \(D\). Although analysis of soil PSD based on fractal geometry (model) has been well studied, as we known, seldom studies focus on the effect of different PSC systems on calculating \(D\) of soils. Estimating PSD should recognize and link differences in scaling properties of particle distributions by PSC systems with pedogenetic processes to interpret the potential of \(D\) as a representation of a PSD. Therefore, in our research, soil PSDs and the fractal dimensions of topsoil (0-5 cm) of 9 different forest densities of MPPs in Mu Us Sandy Land, China were chosen for analysis. _Pinus sylvestris_ var. _mongolica_ is naturally throughout the northern China with average altitudes of 1500 m, and have excellent resistance ability of drought and great survivability. The creation of MPPswas successful in combating desertification in Yulin City, Shaanxi Province, which is situated on the southern Mu Us Sandy Land since the mid-1950s [19]. Moreover, in these regions, frequent and intense wind erosion drastically changes soil PSD [32]. MPPs protect sand land surfaces, thus soil PSD and \(D\) would be affected and also vary with forest densities. The purposes of this research were: (i) to take examples of MPPs to determine the differences of six PSC systems including China (1987), Katschinski (1957), USDA (1993), ISSS (1929), ISO14688 (2002), and Blott & Pye (2012) of characterizing the PSDs and calculate \(D\)s; (ii) to examine the relationship between six PSC systems; (iii) to determine the sensitivities to the changes of \(D\) calculated by six PSC systems in order to discuss the applicability of these PSC systems.
## 5 Discussion
### Comparison with Existing Literature  
Our findings on the progressive impact of corrosion on seismic vulnerability align closely with those reported by [PERSON] et al. [78], particularly for time spans of up to 75 years. For these periods, our results show a deviation of only 3% to 14% from [PERSON] et al.'s findings, providing significant validation for our simulation techniques and methodologies. This congruence underscores the reliability of our degradation rate estimations within expected margins. However, for the 100-year time span, our results consistently predict lower vulnerability compared to [PERSON] et al. This discrepancy suggests that our model may underestimate long-term risks, particularly in marine environments. This difference highlights the need for further investigation into long-term corrosion effects and their impact on seismic vulnerability.  
Our research also aligns with studies by [PERSON] et al. [76] and [PERSON] et al. [46] emphasizing the significant increase in the seismic vulnerability of RC structures due to aging and corrosion. However, our study provides a more comprehensive quantification of this risk increase over time, mainly through cumulative risk ratios. This approach offers a more nuanced understanding of long-term vulnerability compared to previous studies.  
The findings of our study extend the work of researchers like [PERSON] et al. [47] and [PERSON] et al. [51] on bridges by applying similar principles to general RC structures in critical infrastructure. While these previous studies focused on specific structural elements, our research provides a broader framework applicable to various RC structures, offering a more versatile tool for infrastructure risk assessment.