esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis
#### 5.3.3 The Impact of Users  
Then, we examined the impact of the number of users on the total waiting time, as shown in Figure 6. The service number was randomly generated from 4-10. From the figure, we can see that the total waiting time generated by the three methods increased with the number of users increasing. On the one hand, because each user will invoke some services, increasing the users will invoke more services. On the other hand, because the resources of the each edge server are limited, with increasing users, some users have to connect to the remote cloud servers to get services, and the round-trip latency needs to be taken into consideration. Therefore, the total waiting time will be increased by increasing the users. From the experiment, we can conclude that our MESP algorithm outperformed the traditional and random methods.  
From the experiments above, we can conclude that under the same conditions, MESP always outperformed the baseline algorithms. Next, we examine the impact of round-trip latency and service downtime on the total waiting time using the MESP algorithm.  
Figure 4: The total waiting time with a different number of resources.  
Figure 5: The total waiting time with a different number of services in a single-service composition.  
Figure 6: The total waiting time with a different number of users.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 4 Architecture Design and Methodology
### Design of Multi-Slice Architecture  
The deployment of sliced network architecture should improve the network interoperability of wireless infrastructure providers to respond effectively to user requests, and it is closely related to providing a better QoE through adequate QoS. The previous research mainly uses existing queues or other reserved resources in the forwarding hardware, contrary to the SDN paradigm, whose primary goal is to increase network programmability. Considering this, we have started from the facts that, currently:  
* No SDN controller enables standardized management of queues (other than flow assignment to queues);
* OpenFlow and OVSDB represent established standard protocols.  
In a multi-slice wireless environment, we need to define slice organization, which carries a challenge related to management complexity in future networks (Figure 1). Firstly, we need to bind related services that the same service provider most likely offers. It is reasonable to avoid using unnecessary resources to create a separate slice for each service. We believe that grouping services can ensure efficient real-time resource sharing according to business processes. Each group represents a separate slice controlled by its authoritative controller. To clarify the role and importance of service bonding in multi-slice 5G/6G networks, we have shown a use case applied in an intelligent vehicle setup (Appendix A, Table A1).  
We can expect different providers to offer the same groups of services in the market. Our concept supports the possibility of providing a group of services from different providers within a single slice. We isolate services and service providers by leveraging existing network segmentation mechanisms (e.g., VLAN, VXLAN). This solution allows for a high level of scalability since the number of slices is not limited. We propose using VXLANs to technically separate providers that offer the same services group (within each slice, there can be 224 different providers appertaining to a single service group). This approach enables users to access different slices and choose technical and cost-effective offers, which significantly benefits users. Users have the opportunity to choose a service provider for each service group.  
Multi-slice represents the E2E concept implemented in all network segments (RAN, Edge, Core, and transport) [26]. The SDN functionality of the 5G/6G network is available from the edge network to the 6G modem on the customer side. This infrastructure concept allows for fully open access to the service market, which benefits both clients and service providers (Figure 2).
## 4 Experiments and Analysis
### Experiment 4: Positioning Accuracy of the Light-On and Light-Off Scenarios  
The final experiment aimed to determine whether the FiMa-Reader system could overcome the influence of ambient light. We designed the same navigation with docking tasks as in Experiment 3, with a frame rate of 60 Hz, and conducted the tasks in both a light-on indoor environment and a light-off indoor environment. Each experiment was repeated 500 times. In the light-off environment, hardly any visible light could be seen, which would definitely result in different image quality compared to the light-on environment in a usual RGB camera.  
Figure 9 illustrates the positioning results from the two scenarios, with orange points representing the results from the light-on condition and blue points representing the  
Figure 8: The positioning accuracy of different FiMa-Reader output frame rates. (**a**) The x-direction positioning accuracy distribution, unit mm. (**b**) The y-direction positioning accuracy distribution, unit mm. (**c**) The theta position accuracy distribution, unit degrees. The blue dots represent the mean value, while the black squares indicate the presence of outliers.  
results from the light-off condition. The x, y, and theta distributions are almost consistent, indicating that the proposed FiMa-Reader system can work effectively in both light-on and light-off manufacturing applications.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 4 Results
### Predicted Depth Accuracy  
Each machine learning algorithm was applied to three separate datasets obtained during the months of July 2020, August 2020, and February 2021. Scatter plots to visualise the correlation between the predicted values of each machine learning algorithm and the in situ depths are presented in Figures 3-5. The regression line corresponds to the relationship between the SDB and the in-situ data, depicting the line of best fit for the data provided.  
For the month of July 2020, K* gave the best results, while the LR algorithm attained the least accurate values. Furthermore, MLP delivered inaccurate prediction at the shallowest depths (Figure 3). For August 2020, the predicted depth values of each algorithm were more accurate than the July 2020 depth values as all four scatter plots displayed a higher positive correlation with the regression line (Figure 4). K* and LR were still the most and least accurate algorithms, respectively. RF and MLP both exhibit an accurate positive correlation, and both produced good results similar to those by K*. The results predicted for February 2021 followed similar trends as the months of July and August 2020, albeit with a higher accuracy (Figure 5). K*, RF, and MLP produced the most accurate depths while the LR algorithm resulted in a weaker relationship between the predicted and in situ depth values. In general, K* proved to be the best-fitting algorithm out of the four algorithms. This was followed by the RF algorithm, which also outperformed MLP and LR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Month** & **Machine Learning Algorithm** & **Correlation Coefficient** \\ \hline \multirow{4}{*}{July 2020} & Random Forest & 0.88 \\  & Multilayer Perceptron & 0.77 \\  & K-Star & 0.80 \\  & Linear Regression & 0.82 \\ \hline \multirow{4}{*}{August 2020} & Random Forest & 0.89 \\  & Multilayer Perceptron & 0.85 \\  & K-Star & 0.81 \\  & Linear Regression & 0.80 \\ \hline \multirow{4}{*}{February 2021} & Random Forest & 0.94 \\  & Multilayer Perceptron & 0.75 \\ \cline{1-1}  & K-Star & 0.94 \\ \cline{1-1}  & Linear Regression & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient of each machine learning algorithm.  
Figure 4: Scatter plots displaying the accuracy of the predicted depth values compared to the in situ depth values for the month of August 2020.  
Figure 3: Scatter plots displaying the accuracy of the predicted depth values compared to the in situ depth values for the month of July 2020.
## 2 Materials and Methods
### The Experimental Installation Used for Organic Material Pyrolysis  
The laboratory equipment used for biochar production is an experimental installation produced by INMA Bucharest institute, equipped with an intelligent controlling system for better management of the heating process (Figure 4).  
The installation is made of a supporting structure (1) that holds the reactor (2), along with the control and actuation system (3). The reactor is constructed from two stainless steel sheets, insulated between them with high-temperature-resistant ceramic fiber, to minimize energy losses. The casing (4) is equipped with six electric resistors (5) for precise control of the thermal processing. Inside the casing, a biochar retort is positioned to optimize the handling of raw materials (6). An overpressure valve (7) and a thermocouple are installed for accurate temperature monitoring. The biochar retort is constructed from stainless steel and features a door (8), four locking mechanisms (9), and two control handles, for secure operation (10). The exhaust system is equipped with an ice-cooled condenser.  
Addressing the challenges of degraded agricultural lands on slopes requires careful consideration of the unique dynamics of these terrains, where retaining moisture and nutrient-rich topsoil is difficult, often resulting in reduced fertility and productivity. Creating compartmentalized furrows can help prevent topsoil erosion in landscapes where runoff reduces moisture and organic carbon retention, weakening the soil's water-holding capacity.  
The micro-basins are formed using specialized equipment mounted on an agricultural tractor. In this study, an experimental equipment model was used to form compartmentalized furrows on agricultural land with a 7-degree slope, a region particularly prone to soil erosion. The equipment includes chisel-type scarifiers for loosening soil, a hoeing system with blades for weed removal and irrigation channel formation, and five rotating blades, designed to create compartmentalized furrows. Figure 5 provides an in-depth description of the furrow-diking system.  
Water-retention performance was conducted on a 50-square-meter area, with each compartmentalized furrow measuring 4 m in length, 0.4 m in width, and 0.2 m in depth. Each furrow was filled with 2 kg of biochar made from a 50-50 mix of feathers and bird bones. Soil moisture and compaction were measured with the SC900 Soil Compaction Meter (Spectrum Technologies, Aurora, IL, USA) from April to July, and the results were compared with those of another neighboring plot used as a control.  
The water-retention potential in biochar was determined by immersing the produced samples in water for 24 h. The retained water content was calculated based on the difference in mass before and after soaking. This method provides a reliable assessment of biochar's maximum capacity to absorb water, which is a key factor in its application for enhancing soil moisture.  
Figure 4: Experimental installation: (**a**) experimental pyrolysis installation; (**b**) reactor for thermal treatment of waste; (**c**) retort for optimal batch management.
## 2 Methodology
### Improved VI-CFAR
#### 2.2.2 Klq-Cfar  
VI-CFAR performs poorly in multiple-target environments because of its weakly robust detection algorithm. Therefore, the detection algorithm with strong robustness should be used in multiple-target environments. KLQ-CFAR is an order statistic class detector proposed in Ref. [11]. In KLQ-CFAR, the SQ estimator used in OS-CFAR is replaced by the KLQ estimator with higher estimation efficiency. As a result, KLQ-CFAR outperforms OS-CFAR in detection with multiple targets.  
According to [11], when \(r_{1}=6\) and \(r_{2}=3\), KLQ-CFAR has the best detection performance in multiple-target environments, where \(r_{1}\) and \(r_{2}\) denote the number of cells trimmed from the upper and lower end of the reference window, respectively. Therefore, the KLQ detection algorithm with \(r_{1}=6\) and \(r_{2}=3\) is used in multiple-target environments in the improved VI-CFAR.
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 3 Reliability Analysis Model for the Hybrid INS Based on Kriging Surrogate Model
### Kriging Surrogate Model  
According to Kriging theory, the relationship between the output response \(G(x)\) and input variable \(x\) can be represented as Equation (1):  
\[G(x)=f^{\mathrm{T}}(x)\beta+z(x) \tag{1}\]  
where:  
\(f(x)\) is the regression polynomial basis function vector;  
\(\beta\) is the vector of regression coefficients;  
\(z(x)\) is a Gaussian random process with zero mean, and the covariance function is Equation (2):  
\[\mathrm{cov}\big{[}z(x_{i}),z(x_{j})\big{]}=\sigma_{z}^{2}R\big{(}x_{i},x_{j} ;\theta\big{)} \tag{2}\]  
where:  
\(\sigma_{z}^{2}\) is the variance of the Gaussian process;  
\(R\big{(}x_{i},x_{j};\theta\big{)}\) is the correlation function with parameter vector \(\theta\), represented as Equation (3):  
\[R\big{(}x_{i},x_{j};\theta\big{)}=\prod_{k=1}^{m}\exp\left[-\theta k\Big{(}x_ {i}^{k}-x_{j}^{k}\Big{)}^{2}\right] \tag{3}\]  
with \(x_{i}^{k}\) and \(x_{j}^{k}\) as the k-th elements of vectors \(x_{i}\) and \(x_{j}\), respectively.  
Given a training sample set of size \(S_{0}\), the unbiased estimate of \(G(x)\) and prediction error are defined as follows Equations (4) and (5):  
\[\mu_{G}(x)=f^{\mathrm{T}}(x)\overset{\wedge}{\beta}+r^{\mathrm{T}}(x)R^{-1} \bigg{(}Y-F\overset{\wedge}{\beta}\bigg{)} \tag{4}\]  
\[\sigma_{G}^{2}(x)=\sigma^{2}\bigg{[}1+u^{\mathrm{T}}(x)\left(F^{\mathrm{T}}R^{ -1}F\right)^{-1}u(x)-r^{\mathrm{T}}(x)R^{-1}r(x)\bigg{]} \tag{5}\]  
where:  
\(\overset{\wedge}{\beta}\) is the estimated value of \(\beta\);  
\(r(x)\) is the correlation function vector between the training sample points and the prediction point;  
\(Y\) is the response of the training sample;  
Figure 9: Installation of the inertial group on the missile. Note: Projectile coordinate system: \(o-x_{b}y_{b}z_{b}\) Inertial group virtual body coordinate system: \(o-x_{b\text{t}0}y_{b\text{t}0}z_{b\text{t}0}\) Inertial group body coordinate system: \(o-x_{b\text{t}}y_{b\text{t}2}z_{b\text{t}}\).  
\(F\) is the regression matrix, Refer to Equations (6) and (7) for definition.  
\[F=\left[f^{\mathrm{T}}\Big{(}x^{(1)}\Big{)},f^{\mathrm{T}}\Big{(}x^{(2)}\Big{)}, \cdots,f^{\mathrm{T}}\Big{(}x^{(N)}\Big{)}\right]^{\mathrm{T}} \tag{6}\]  
\[u(x)=F^{\mathrm{T}}R^{-1}r(x)-f(x) \tag{7}\]  
To improve the model's prediction accuracy, this study uses the mean square error method to train the Kriging model by selecting points (Equation (8)) with the highest prediction error as new training points, updating the Kriging model:  
\[x_{new}=\mathrm{argmax}^{2}(x) \tag{8}\]  
where \(s\) is the standard deviation.  
The convergence condition for building the Kriging model is determined as follows Equation (9):  
\[E=\left|T_{M}-T_{1}\right|/T_{1}\leq 10^{-3} \tag{9}\]  
where \(T=\mathrm{max}s\), \(T_{1}\) is the maximum standard deviation before adding a point. The smaller the value of \(E\), the more accurate the Kriging model prediction becomes. Figure 10 shows the solution process of the Kriging surrogate model.
## 2 The Principle of Active Monitoring  
Figure 1a shows the smart aggregate employed in the proposed monitoring system, which was innovatively introduced by [PERSON] [8]. SA's are generally utilized in pairs in SHM practice. According to the direct and inverse piezoelectric effect of inner piezoelectric patch, one SA works as a sensor and the other works as an actuator among the pair of SA's, which constitute a representative function unit.  
As shown in Figure 1b, the monitoring system consists of a signal generator, a power amplifier, an oscilloscope, and the function unit in concrete. Harmonic voltage and plus voltage signals generated by signal generator are applied to the actuator after magnified by the power amplifier. Then, electric signals are transformed into vibration signals in the SA actuator with the inverse piezoelectric effect. Subsequently, the vibration signals propagate in the concrete in the form of stress wave until they are captured by the other SA. As a result of direct piezoelectric effect, vibration signals are transferred into electric signals by the sensor, and finally, the electric signals are displayed and recorded by the oscilloscope. The monitoring method relies on wave propagation theory [28]. By analyzing the response signals, the condition of the material along the propagation path can be diagnosed.  
Figure 1: Monitoring system. (**a**) Smart aggregate with cable; and, (**b**) Test scheme.
## 4 Conceptual Design of BESPECIAL
### First Development Stage
#### 4.1.3 Application of Optical Character Recognition to the Dyslexia Clinical Report of the Students  
The clinical reports of dyslexic students are the second input to BESPECIAL. From them, the information given by the experts about the status of the disorder for each student will be collected. In order to automate the extraction of this information, an algorithm based on optical character recognition (OCR) will be devised. Medical reports are generally not released in a digital format, requiring to be photographed to be passed to a computer, or, however, their format does not permit easy modifications (like pdf). The platform must, thus, be able to take files in different formats as input and interpret them. The system will then use the OCR algorithm provided by Google Vision API to recognize and extract the text of the medical reports from such files. After that, the three typical steps of text pre-processing will be applied to the text extracted with OCR. These are: removing stop words, tokenization and stemming. The first one aims at removing commonly used words that are not useful to characterize the content of the document, such as articles and conjunctions. The second one, instead, splits the text into single words (tokens), so as to ease the content analysis. Finally, the third one aims at reducing each word to its root, allowing inflected forms of the same word to be grouped and then treated as a single element. Once the  
Figure 7: Example of two inquiries of the questionnaire, about the issues experienced (**a**) and the supporting methodologies considered as useful (**b**) by dyslexic students.  
clinical reports will be processed by the OCR module, useful information can be extracted from them. For example, it may be possible to understand the type of dyslexia of each subject or their specific needs.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 6 Realized Demonstrator
### Discussion on LoRa Communication Channels  
LoRa technology uses orthogonal configurations, and this should allow the transmission of signals with different SFs in the same frequency channel simultaneously, without interference. In fact, signals with an SF different from that of the receiver appear to the latter as noise. One could, therefore, hypothesize using different SFs to make connections between the different PRs and guarantee the simultaneous transmission of Blind messages.  
However, signals with different SFs are not strictly orthogonal, but rather pseudo-orthogonal, that is, the correlation function does not return exactly zero. Consequently, a packet sent with a defined SF to be correctly demodulated must exhibit a signal-to-interference-plus-noise ratio (SINR), expressed in dB, that is sufficiently high if compared to an interfering packet that has a different SF value.  
In Ref. [19], a table is given that expresses the value of the minimum required SINR, which is dependent on the SF of sent and interfering packets. The minimum SINR that must be guaranteed decreases as the overlapping time decreases. The authors took the worst case into account, since, in the case of logic selectivity, the Blind messages have to be transmitted simultaneously. The solution based on the use of different SFs appears, however, to not be feasible. In fact, if a PR has to both transfer and receive a Blind message, inevitably the transmitter will interfere with the receiver because of their physical proximity, even if a different SF was used for the transmitter and the receiver. In other words, it is not possible to guarantee an adequate SINR unless using antennas that transmit in one direction, replacing the omnidirectional ones used for the realization of the prototypes.  
In order to overcome this limitation, a frequency division approach has therefore been used for transmissions, choosing a different central frequency for each of the communication channels that must be guaranteed according to the provisions in [40]. As an example, the sensor node in the primary station and the master of the PR1 will communicate with a central frequency of 868 MHz; the slave of the PR1 and the master of the PR2 at 867 MHz and so on, without interference [41].
## 3 Results
### Water Quality  
Selected anions (chloride, nitrite-N, nitrate-N, sulfate, and phosphate) were analyzed and quantified at the end of the experiment. Additionally, the concentrations in tap water were analyzed containing 7.7 mg/L CCl\({}^{-}\), 0.21 mg/L NO\({}_{3}\)-N, 0.75 mg/L SO\({}_{4}\)\({}^{2-}\), and below limits of detections for NO\({}_{2}\)-N and PO\({}_{4}\)\({}^{3-}\) (Supplementary Table S2). There was no significant difference (\(p>\) 0.05) in the concentrations (mg/L) of chloride, phosphate, sulfate, or nitrate between hydroponics and aquaponics treatments (Table 4).  
The concentration (mg/L) of chloride was higher (\(p<\) 0.05) in the aquaponics circulating water than in RAS water, but the concentration of other anions did not differ between the treatments (Table 5).  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Element (mg/L)** & \multicolumn{2}{c}{**Aquaponics**} & \multicolumn{2}{c}{**Hydroponics**} \\ \hline Chloride & 24.80 & \(\pm\)14.68 & 14.24 & \(\pm\)6.19 \\ Phosphate & 0.10 & \(\pm\)0.11 & 8.07 & \(\pm\)6.20 \\ Sulfate & 69.06 & \(\pm\)49.74 & 85.32 & \(\pm\)57.03 \\ Nitrate-N & 3.44 & \(\pm\)1.96 & 4.51 & \(\pm\)2.74 \\ \hline \end{tabular} Values are means \(\pm\) SD, \(n=3\). There were no statistically significant differences between the treatments (independent sample \(t\) test, \(p>\) 0.05). Nitrite-N was below the LOD (0.13 mg/L) in both treatments.  
\end{table}
Table 4: Concentrations of chloride CCl\({}^{-}\), nitrate-N NO\({}_{3}\)-N, sulfate SO\({}_{4}\)\({}^{2-}\), and phosphate PO\({}_{4}\)\({}^{3-}\) (mg/L) in water samples taken on the last day (day 42) of the experiment in aquaponics and hydroponics deep water culture units. For aquaponics treatment spinach (_Spinacia oleracea_) was grown in a coupled aquaponic system together with rainbow trout (_Oncorhynchus mykiss_).  
Figure 1: The mean concentration (mg/L) \(\pm\) SD (\(n=3\)) of (**a**) total ammonium nitrogen (TAN) (**b**) nitrite, (**c**) nitrate, and (**d**) pH in RAS and aquaponics treatments during the 42-day experiment. For the aquaponics treatment spinach was grown in a coupled aquaponics system together with rainbow trout (_Oncorhynchus mykiss_).