esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed LOADng Enhancement for IoT Networks
### LOADng-IoT Data Message Forwarding  
In the network operation with the use of the proposed LOADng-IoT, the nodes that intend to send data messages should verify its Routing Set and execute the route discovery process, if necessary. After constructing the routes, simple messages and Internet messages can be sent to its destinations through the next hop node. In the case of simples messages, as the LOADng constructs only one path to each destination, it is not possible to compare the best route for sending (the best path selection is made during the process of route discovery). However, to send an Internet message, the node should to select the best path based on the selected routing metric since the Internet route discovery process can create several routes to different INs. Thus, it is possible to choose the best path among those available. The nodes that receive a data message (both simple and Internet) should consult its Routing Set to find the next hop to the destination node. This process should continue until the data message is delivered.  
Independent of the message type, it is possible for a broken route to occur during the message forwarding process. In this case, the intermediate node that was not able to forward the message detects the broken path, queues the data message, and starts a new route discovery process according to the message type. If the message is simple, the node should begin a normal route discovery using the standard LOADng procedure. However, if the message is directed to the Internet, the node should start an Internet route discovery following the process described in Section 4.4. Thus, the node should consult its IRC set (if enabled) and send an RREQ-IoT unicast or broadcast following whole the process. In both cases, if the route is constructed successfully, the queued data message is forwarded normally. Otherwise, if it is not possible to reconstruct the path, the node should generate an RERR according to the data message type and send it to the data message originator.  
Figure 5: Flowcharts of RERR control messages with code 253 processing. Dotted lines represent the flow used only when IRC is in use.
## 3 Results  
### BPN Values for Different Binders and Aggregates of Various Sizes Before and After Polishing  
Figure 6 illustrates the BPN values measured before and after ten hours of polishing cycles using the British Wheel, highlighting three different aggregate sizes--HFST, medium, and coarse--along with various binders.  
Among all the aggregates, CB exhibited the highest BPN values for the original HFST size both before and after polishing due to the rougher surface morphology (texture) properties of this aggregate. Rhy and Flint also yielded comparable results initially, with similar BPN values across all samples. However, samples containing PG modified binders and emulsions experienced a more significant decrease in BPN levels after ten hours of polishing, although there were exceptions, such as Rhy and Meramec. For instance, the BPN value for Rhy in the HFST size decreased by only 12% after polishing with PG88-16, which was less than the 17% reduction noted with epoxy. In the case of the medium-sized aggregates, the transition from epoxy- to asphalt-based binders resulted in a more pronounced decline in post-polish BPN values compared with the HFST size. In contrast, the coarse-sized aggregates exhibited a more moderate decrease in BPN values across all binders when compared with both HFST and medium-sized aggregates. This is especially true for both emulsions, indicating that emulsions may perform better with coarser aggregates. This difference can be attributed to the distinct nature of epoxy, which is thermosetting, and the asphalt-based binders with viscoelastic properties. The bonding between the epoxy and the aggregate is stronger due to these properties, helping to retain the finer-sized aggregates. When shifting to asphalt-based binders, the use of coarser aggregates can reduce chip loss during polishing cycles [38].  
Figure 6: BPN values measured before and after polishing cycles.
# A Method to Reduce Non-Nominal Troposphere Error
## 5 Method to Reduce the Non-Nominal Troposphere Error
### Current Satellite Selection Method  
A poor selection algorithm can lead to poor satellite geometry or increase the non-nominal troposphere error. This section will describe several selection methods that may be suitable for non-nominal troposphere.  
1. Elevation method: The non-nominal troposphere error decreases with increasing elevation angle. The elevation method sorts the satellites by elevation angle and retains the k satellites with the larger values which have relatively smaller non-nominal troposphere errors. The aim of the elevation method is to minimize the non-nominal troposphere error in the satellite constellation. However, removing the lowest satellites can significantly increase the Vertical Dilution of Precision and in turn increase the VPL, thereby impacting the GBAS integrity [7]. Therefore, the elevation method is not an optimal method for the non-nominal troposphere. The VDOP is related to the satellite geometry of the visible satellites involved in the positioning solution. The VDOP of the \(m\) satellites is [20] \[VDOP=\sqrt{g_{33}}\] (15) \[G_{m}=\left(H_{m}^{T}H_{m}\right)^{-1}\] (16) where \(g_{33}\) is the diagonal element of the third line and the third column of \(G_{m}\),\(H_{m}\) is the observation matrix of \(m\) satellites. Based on the computation of the VDOP, the low elevation satellites are often quite important for good vertical geometry [7].
2. GDOP method: GDOP is an important evaluation factor for the satellite geometry, and a smaller GDOP indicates a better satellite geometry [1]. In 2016, [PERSON] of the University of Rhode Island found that the satellite subset that minimizes the GDOP consists of approximately 30% low-elevation satellites and 70% high-elevation satellites [9]. Although the GDOP method can optimize the satellite geometry and minimize the GDOP, the use of 70% low-elevation satellites will increase the non-nominal troposphere error in the constellation, resulting in a higher VPL and impacting the GBAS integrity.  
Brute method:  
The brute method examines all possible combinations of k out of N satellites to determine the best performance. The brute method can choose the optimal satellite subset which minimizes the output. In this paper, the goal is to minimize the VPL under non-nominal troposphere, and thus the criterion for satellite selection is the minimum VPL. This method is optimal in terms of returning the best possible VPL but is distinctly non-optimal in terms of its computational cost [8].  
4. Greedy method:  
The greedy method is similar to the brute method [21]. This method removes only one satellite at a time and uses the optimal satellite subset to evaluate the next iteration. The iteration process continues until the optimal subset of k satellites is selected. The selection criterion of the greedy method is also the minimum VPL.
## 2 Data and Methods
### Methods
#### 2.3.2 Experimental Design  
In this study, three sets of soil moisture data, namely, SMAP_X, OBS_X, OBS_Daily_X were used as inputs for SM2 RAIN to estimate precipitation. Here, X stands for a certain sampling time, with X = A, X = D, and X = AD, each corresponding to satellite overpass time of ascending orbit, descending orbit, and both orbits. As such, SMAP_X, OBS_X, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Lower Boundary** & **Upper Boundary** \\ \hline \(Z^{*}\) (mm) & 1 & 400 \\ \(a\) (mm/d) & 0 & 1800 \\ \(b\) (\(\sim\)) & 1 & 50 \\ \(T_{pot}\) (\(\sim\)) & 0.05 & 0.75 \\ \(T_{base}\) (\(\sim\)) & 0.05 & 3 \\ \(K_{c}\) (\(\sim\)) & 0.02 & 1.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Upper and lower boundaries for parameters in the SM2 RAIN algorithm.  
Figure 4: The accumulated precipitation curve of IMERG grid where CMA station is located (Pre_IMERG_NQ) and the regional average IMERG precipitation (Pre_IMERG_RM) during (**a**) JJAS 2015, (**b**) JJAS 2016, and (**c**) JJAS 2017.  
OBS_Daily_X each corresponds to the original SMAP soil moisture retrieval with coarse intervals, the CTP-SMTMN surface soil moisture with the same sampling interval as SMAP, and the CTP-SMTMN surface soil moisture with a finer sampling interval of 1 day. Upon these datasets, two sets of experiments were implemented to investigate the uncertainties in SM2 RAIN estimated precipitation. First, the impact of SMAP retrieval accuracy was quantified by comparing precipitation estimates from SMAP_X and OBS_X. Second, the impact of the SMAP original retrieval interval was quantified by comparing precipitation estimates from OBS_X and OBS_Daily_X, and this is so designed to eliminate the impact from retrieval accuracy. Note that the impact of varying retrieval intervals is also worthy studying, yet it is beyond the scope of the current study and will be addressed in future studies. Finally, the difference between satellite orbits and the added value of combining two orbits in estimating precipitation were obtained by comparing x_A, x_D, and x_AD (x = SMAP, OBS, OBS_Daily).
# An Energy-Balanced Geographic Routing Algorithm for Mobile Ad Hoc Networks
## 1 Introduction
In Reference [31], an energy-balanced routing algorithm based on a probabilistic transmission model (EGPSR) is proposed. This algorithm divides the forward region into four parts with equal area, thus prolonging the network's lifetime. Although the proposed algorithm considers the energy consumption of the nodes in mobile ad-hoc networks, it ignores mobility, the most basic feature of mobile ad-hoc networks. In Reference [32], a routing algorithm considering node energy and mobility, SU-GPSR, is proposed to solve the node mobility problem ignored by Reference [31]. However, the SU-GPSR algorithm does not take into account the relationship between the remaining energy and the energy consumption rate of the node, and the node energy is therefore not balanced. In this paper, we propose an improved GPSR algorithm named EM-GPSR (Energy and Mobility GPSR), by integrating the node mobility and the node remaining energy. With respect to the node mobility, this work firstly divides the forward region according to the remaining lifetime of the link. Then, it selects the next-hop node in the candidate region according to the remaining lifetime of the node, the distance from the node to the destination node, etc.. Regarding the node energy balance, when taking the energy consumption rate into account, the selection of the next hop favors the node with the longest remaining lifetime. Simulation results show that, compared with conventional greedy perimeter stateless routing (GPSR) and the SU-GPSR routing algorithms, the proposed algorithm can lead to a lower end-to-end delay, longer service time, and higher transmission efficiency for the network.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### Stopping Distance Without Wind (DR2)  
The second derived requirement states that each drone must compute its maximum stopping distance under non-windy conditions, considering its velocity. This requirement is based on three environmental assumptions, namely A8 to A10. The first assumption states: _\"All drones operate autonomously, eliminating human reaction delays.\"_ This implies that drones are capable of making real-time decisions independently, without relying on manual input. For instance, if a drone detects a nearby vehicle, it is expected to adjust its flight path immediately without human intervention.  
The requirement presumes predictable braking responses. However, if the assumption about instant reactivity is inaccurate, the requirement becomes flawed because it neglects possible delays introduced by sensor processing time, actuator latency, or variations in drone payload. The RTD that results in this scenario would require modifying the requirement to account for real-world delays and to explicitly define an acceptable latency threshold, both of which are addressed by environmental assumptions A9 and A10..  
**RTD:** When these assumptions are not properly validated, the requirement may remain incomplete, potentially leading to incorrect stopping distance calculations. This type of debt is classified as _incomplete assumption debt_, referring to technical debt caused by the absence or incompleteness of essential environmental assumptions. Although this issue is also partially related to the _unvalidated assumption debt_ category, the more appropriate classification is incomplete assumption debt, since the initial requirement fails to account for critical factors such as delay in response time, sensor lag, or actuator performance.
## 3 Results and Discussion  
### FTIR-ATR and Confocal Raman Microscopy Analysis  
The FTIR results allowed the identification of the type of coating present in the can samples of this study, on both the internal and external side. Only the best matches with the libraries were selected with a high quality index (HQI) higher than 90.  
In the three samples, an internal, polyester-type coating was identified. In sample CM2, a composite spectrum with two types of polyester coating could be differentiated with an HQI of 96.17; one of them being identified as polyethylene terephthalate (PET). This identification coincides with the bibliography since the photoactive layer of TiO\({}_{2}\), responsible for the white color, could be applied to the polymer to prevent oxidation of the PET [11]. Figure 1 shows the IR spectrum on the internal side of the sample CM2 (black line) overlaid with the first entry of the IR spectral libraries corresponding to the composite spectrum (red line). In addition, this identification could be confirmed by confocal Raman microscopy. This technique provided a complete characterization of the coating layers by imaging the different layers, and it was confirmed that the layer in contact with the food was PET (Figure 2).  
Figure 1: IR spectrum corresponding to the internal side of the sample CM2 (black line) compared to the first entry of the spectral libraries corresponding to the composite polyester spectrum (red line). Absorbance vs. wavenumber (cm\({}^{-1}\)).  
Polyester resins are produced by condensing an acid with one or more alcohols, followed by copolymerization with one or more cross-linking agents [12]. For example, PET is produced in an esterification reaction where ethylene glycol reacts with terephthalatic acid. PET is a linear, transparent thermoplastic polymer which has the capacity to crystallize under certain, controlled conditions [13].  
Regarding the external side, the bright yellow coating of samples CM1 and CM3 was identified as epoxy resin, while, in sample CM2, with an ink printing, styrene/acrylic copolymer was identified. Due to recent calls to try to eliminate commonly used epoxy resins, they are gradually being phased out as internal coatings for food-contact materials, and alternatives are being sought, such as polyester resins. Acrylic resins are used principally for external can coatings [12], providing good color retention and heat stability [14].
## 3 Drone Swarms as Networked Control Systems  
Drone systems are mission-oriented in most cases. The missions might have different scales and complexities, requiring the deployment of networks with different strategies, as shown in Figures 1-3. In a simple and low-scale mission, a drone system may encompass a single drone or a group of non-interacting drones, associated with a ground control center (c.f. Figure 1). For larger scale missions, multiple interactive drones, i.e., a drone swarm, may perform better through consensus and swarm intelligence (c.f. Figure 3). An overly complex system may encompass several drone swarms connected via a ground or satellite platform, via which they can share information. In such missions, a drone swarm may be modelled as a Networked Control System (NCS) [26].
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 2 Materials and Methods
### Statistical Analysis and Data Evaluation  
To evaluate the impact of gait-synchronized vibrotactile stimulation on the walking ability of lower limb amputees, we calculated the median and the interquartile range (IQR) for the entire cohort as well as the mean performance of the individual subjects at baseline and follow-up for each walking test (see Appendix A, Tables A1 and A2). Furthermore, we determined the absolute and relative changes between the two measurements for both the entire cohort and individual subjects across all gait tests (see Appendix A, Tables A1 and A2). The statistical analysis of the data was conducted using Microsoft Excel (Version 16.91) and Matlab (MathWorks, Version R2024a). Due to the small sample size, no assumptions regarding the normal distribution of the sample could be made; hence, the Wilcoxon signed-rank test was utilized for further statistical analysis. The significance level was set at a \(p\)-value of <0.05.  
The assessed performance data at baseline and follow-up were compared to each other. The results were evaluated regarding the presence of Minimal Detectable Changes (MDC) and Minimal Clinically Important Differences (MCID) for a unilaterally amputated cohort (see Table 1). Additionally, correlation analyses were conducted between the individual performance of the subjects (\(\Delta\mathrm{t}_{1}-\mathrm{t}_{0}\)) and both the usage time of the VTFS at home and the time since amputation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Gait Test** & **MCID** & **MDC** \\ \hline Timed “Up and Go” Test & n.a. & 3.6 s [38] \\ \hline Four Square Step Test & n.a. & 2.0 s [39] \\ \hline
10 Meter Walk Test & 0.21 m/s [40] & n.a. \\ \hline
2 Minute Walk Test & 37.2 m [40] & n.a. \\ \hline \end{tabular}
\end{table}
Table 1: Minimum Detectable Change (MDC) and Minimal Clinically Important Difference (MCID) values for the Timed Up and Go (TUG), Four Square Step Test (FSST), 10 Meter Walk Test (10 MWT), and 2 Minute Walk Test (2 MWT) for unilateral amputees. To assess the clinical significance of the changes in the evaluated gait parameters in this study, the results were compared to existing MCID values. In cases where no MCID value for the respective test had been reported in the literature, the Minimum Clinically Detectable (MCD) value was utilized (n.a. = not available).
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 6 Time History Analyses on 3D Models and Comparison to IIS Design Spectrum
### Linear THA  
A preliminary comparison is given in Figure 8a, where the _IIS design spectrum_ (displacement ratio vs. fundamental period of the IIS) is contrasted to the blue markers, representing the results of linear THAs carried out on the 3D FE models of the six design solutions (A-E). The markers are obtained as average values of the peak displacements exhibited under the seven seismic inputs and are plotted as a function of the fundamental period \(T_{1}\) of the structural complex after addition. The markers are almost coincident with the curve derived from RSA on lumped mass models. Therefore, it seems that, when the LS of the IIS complex remains in the elastic field, the _IIS design spectrum_ provides a very good estimate of the expected behaviour in terms of displacement demand.
## 4 Experiments and Discussions
### Comparison of Perturbation Pixel Detection Approaches  
As discussed in the previous section, the effectiveness of the perturbation pixel detection approach directly affects the ability of RAEs to be perfectly restored to the original image. Consequently, we conducted a comparative analysis of several approaches to validate and illustrate the effectiveness of the proposed D-CSDPP.  
As shown in Table 1, by applying high-pass filtering separately to the RGB channels and calculating the difference matrix relative to the original channels (using the same method as described in Figure 6, which will not be elaborated on further), 88.42% of the RAEs to successfully detect the positions of the perturbed pixels. Utilizing only the HSV channels results in a detection success rate of 91.16%. Furthermore, if we simply com  
Figure 7: Data extraction and image restoration (the pixels filled with green color remain unchanged).  
bine the six channels, the success rate rises to 97.66%. By using the proposed weighted summation method, the success rate can be increased to 98.85%. Additionally, we conducted experiments utilizing a median filter similar to the Laplacian operator.However, this method proved ineffective (with a success rate of 0) because the salt-and-pepper noise present in the image is excessively amplified during this process. Figure 8 shows the visual results of the difference matrices generated by different approaches. Since the final difference matrix of each approach is obtained by summing multiple channel difference matrices, its maximum values exceed 255. Therefore, the value range is stretched to [0, 255] for visual effects.
## 2 Materials and Methods
### Feature Importance and Mean Dropout Loss  
The last step of the analysis is determining the variable, i.e., feature importance from the proposed algorithms. For estimating the relative influence of predictor variables, we rely on MDL (Mean Dropout Loss) [40; 41; 42]. The MDL is a commonly used metric in ML for estimating the relative influence that individual variable (s) have on model performance. For understanding, we first explain the objects used in the Algorithm 1. The \(X\) represents a matrix of input variables (features) with dimensions of \(n\times\text{p}\), whereas the \(n\) is the number of observations, and \(p\) is the number of variables in the processed dataset. The \(y\) is the output vector of dimension \(n\), which in this case, represents the component PC1. The \(\mathcal{M}\) is the ML model, and \(\mathcal{L}\) is the loss function, in this case, MSE. The algorithm output is feature importance point value \(\mathcal{F}\).  
```
Input \(X\), \(y\), \(\mathcal{M}\), \(\mathcal{L}\) Output\(\mathcal{F}\) feature importance Steps
1 Train the baseline model\(\mathcal{M}\)  Model \(\mathcal{M}\) training using training set (\(X_{train}\), \(y_{train}\))  Perform prediction on a holdout dataset \(X_{\text{hold}}\) for obtaining \(\mathcal{M}(X_{hold})\)  Calculate baseline loss function \(\mathcal{L}\): \(\mathcal{L}_{\text{baseline}}\) = \(\mathcal{L}(\textit{{{th}}}_{\text{{{th}}}},\textit{{{y}}}_{\text{{{baseline}}}})\)
2 Intillise feature importance \(\mathcal{F}\)  Let \(\mathcal{F}\) be a vector with length \(p\) such that \(\mathcal{F}\) = [0, 0..., 0]
3 Calculate MDL for each feature  for each feature \(j\) in {1, 2, 3,... \(p\)}  create validation on a holdout set \(X_{hold}^{(j)}\) where \(j\)-th feature is dropped after 50  permutation set with its mean value such that: \(X_{hold}^{(j)}=X_{hold}\) with \(X_{hold}\), dropped  perform prediction on \(X_{hold}^{(j)}\) to obtain \(\hat{g}^{(j)}\): \(\hat{g}^{(j)}=\mathcal{M}\left(X_{hold}^{(j)}\right)\)  calculate the loss \(\mathcal{L}^{(j)}\): \(\mathcal{L}^{(j)}=\mathcal{L}\left(\textit{{{y}}}_{\text{{{hold}}}},\hat{g}^ {(j)}\right)\)  perform MDL for the \(j\)-th feature: \(MDL_{j}=\mathcal{L}^{(j)}-\mathcal{L}_{\text{{baseline}}}\)  assign value for MDL\({}_{j}\) based on \(\mathcal{F}_{j}\): \(\mathcal{F}_{[j]}=MDL_{j}\) end for
4 (optional) Normalisation of feature importance \(\mathcal{F}_{j}\):  normalisation of features \(\mathcal{F}_{norm.}=\frac{\mathcal{F}}{\sum_{j=1}^{n}\mathcal{F}_{[j]}}\)
5 Return
```  
**Algorithm 1** Determining the feature importance based on Mean Dropout Loss.