esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Applications of Opportunistic Routing Protocols
#### 5.3.2 Amor  
Lately, in 2020, [PERSON] et al. proposed Adaptive Motion-based Opportunistic Routing (AMOR) [56], an application of the opportunistic routing protocol that makes use of contact opportunities by carrying the data during the movement of the carrier. Hence, motivated by this mobility features of the carrier, this protocol offers an adaptive mobility-based opportunistic routing mechanism, which specifically works on opportunistic networks. Based on the data communication features, this protocol constructs a data forwarding priority model and defines the data transmission rules according to the nodes' communication range. In addition, it proposed a tradeoff between the system overhead and transmission efficiency. In order to select a potential forwarder node, a free mobility level model is also proposed in this protocol. The simulation results provided with this protocol showed that this algorithm comparatively increases packet delivery ratio, and successfully decreases delivery latency. However, the authors did not discuss the energy consumption properties of this protocol.
## 5 Test Data Analysis
### Pile-Soil Load-Sharing Ratio  
The load-sharing ratio is a parameter. It is used to show the degree of interaction between piles and soil in the foundation and functions as a design parameter for composite foundations. The pile and the soil between the piles share the load, except when the piles bear most of the load. On the contrary, it is difficult to find the reinforcement effect of composite foundation when the soil between piles bears most of the load. In the test section, the earth-pressure box was placed at the top of the pile to test the earth pressure between the piles. This is to ascertain the load-sharing situation of the bag grouting pile. The test data shown in Figure 7 indicate that increased filling load leads to increased soil pressure on the pile top and between piles. When the fill height increased to 5.62 m, the earth pressure on the pile top and between piles reached its peak and then rebounded slightly. The maximum recorded soil pressure for the basement pile top was 377 kPa, and the maximum between piles was 113.47 kPa.  
Figure 8 shows the main section's pile-soil stress ratio. An increased filling load led to an increased pile-soil stress ratio. Afterload stabilization, the pile-soil stress ratio decreased. When the soil between piles was compressed and consolidated, the piles and the soil between piles formed a composite foundation. Under the condition of constant displacement ratio, the soil between piles bore more of the load share, so the pile-soil stress ratio tended to decrease. At the end of the whole process, the pile-soil stress ratio was 2.4.  
Figure 6: Bag-grouting-pile section subsidence curve.  
Figure 7: Pressure curves of the bag-pile section pile top and the soil between piles.  
Compared with the previous research results [12; 13], the measured pile-soil stress ratio is similar to that of the flexible composite foundation such as gravel pile. However, the pile-soil stress ratio is significantly lower than that of the tubular pile and CFG pile [14; 15]. Therefore, we calculated that the load-sharing ratio of the composite foundation pile of the bag grouting pile was 18.7-47.9% after converting the measured pile-soil stress ratio into the load-sharing ratio. This, from a numerical perspective, shows that the design made reasonable use of pile-soil co-bearing action.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 3 Numerical Simulations and Discussion
### Mission Applications
#### 3.3.1 Case of DT with \(\beta=0.3\)  
The first mission example refers to a DT obtained with a medium-high performance E-sail with \(\beta=0.3\). According to Figure 3, the minimum flight time is \(\Delta t\simeq 4.74\,T_{0}\), while the aphelion point has a radial distance \(r_{a}\simeq 3.43\,r_{0}\) and its polar angle is \(\theta_{a}\simeq 155\,\)deg; see also Figure 4. The polar form of the COFT is shown in one of the plots included in Figure 5, while the time variations of the state variables are reported in Figure 11, where the red star indicates the aphelion point reached during the transfer trajectory.  
The analysis of the curves reported in Figure 11 confirms that the aphelion point is reached right in the middle of the transfer, and indicates that the variation of \(\{r,\,\theta,\,u,\,v\}\) is essentially symmetrical over time. This is, of course, a consequence of the spacecraft retracing the same heliocentric trajectory after reaching the aphelion point, as mentioned in the previous section. In the same figure, note how the transverse component \(v\) of the spacecraft velocity decreases with time (with a zero value in correspondence of the aphelion, as expected), while the radial component \(u\) presents three points in which its value is zero, that is, the \(\{\)start, aphelion, arrival\(\}\) points.  
Figure 11: Time variations of the state variables in a DT scenario with \(\beta=0.3\). Black circle \(\rightarrow\) starting point; red star \(\rightarrow\) aphelion point; black square \(\rightarrow\) arrival.  
Regarding the optimal control law, simulations indicate that the E-sail propulsion system is always on during a DT, so that \(\tau=1\) for \(t\in[t_{0},\,t_{f}]\). Instead, \(\alpha_{n}\) changes during the transfer and its time variation has the symmetrical form sketched in Figure 12. The interesting aspect that emerges from this figure is that the value of the pitch angle is essentially constant throughout most of the COFT. In fact, from Figure 12 it can be seen that \(\alpha_{n}\simeq 45\deg\) in about 70% of the transfer. This is an interesting behavior, common to the other DTs analyzed in the simulations, because an almost constant value of pitch angle indicates a nearly constant sail attitude in an orbital reference frame. In the context of optimal control laws, a different behavior appears in a transfer with a single SWA, as discussed in the next section.
## 3 Results
### Thin Film Stress  
Before and after the deposition of the HfO\({}_{2}\) single layers, the curvatures of the Si-substrates were measured. The film stress was calculated for each sample using Equation (1). Figure 3 shows the results of the stress calculation for Si-substrates coated with 480 nm HfO\({}_{2}\) at 100 \({}^{\circ}\)C and various beam voltages of the assisting ion beam source. The data are approximated by a spline fit. As can be seen, the evolution of the film stress can be clearly divided into two regions: (I) stress build-up and (II) stress breakdown. In region (I), a monotone increase in the film stress is observed. For non-extraction of a beam (U\({}_{\text{assist}}\) = 0 V), the stress starts with an initial value of about 0.6 GPa. With the extraction of the beam, the stress increases continuously with the increasing voltage of the assisting ion beam source. This trend can be described approximately by a cubic function (best fitting of the data is achieved by a power of 2.81) up to a certain critical stress of about 3-3.5 GPa for a critical voltage U\({}_{\text{crit}}\) of about 550-575 V. After a maximum observed stress, the thin film stress drops down in region (II) with a further increasing voltage. The measured stress was below the initial film stress, with a value of about 0.5 GPa.  
In Figure 3b, the stress fit function is shown for the 100 nm HfO\({}_{2}\)-films as well as for the 480 nm films, for comparison. As it can be seen in the case of the 100 nm films, the initial stress level is smaller and almost constant with increasing voltage up to 250 V. In consequence, region (I) is shifted to larger voltages and the maximum critical stress is not reached for the critical U\({}_{\text{crit}}\) for 100 nm, as can be seen in Figure 3b.
## 3 Phase I : Proposed Precoding-Based Source to Relay Transmission  
In this section, we focus upon transmission phase I from the \(L\) sources to the relay node. Each of the \(L\) sources transmits a lattice point from the same lattice towards the relay nodes. The received signal at the relay is a noisy linear combination of the lattice points, with linear coefficients given by the channel gains. As explained in Section 1, the concept of CF relies on the lattice property that an integral linear combination of lattice points is another lattice point. However, the received signal at the relay does not necessarily have integral coefficients, as the natural channel gains are not necessarily integers. Therefore, the closest lattice point to the received signal may or may not be an integral linear combination of the original lattice points, depending on the additive noise and the \"self-noise\" contributed by the non-integral nature of the channel. Since additive noise is inherent to the system, the self-noise due to the non-integral nature of the channel should be minimized. In this section, the problem of obtaining an integer linear combination of original signals from the received signal is formulated, and subsequently, an integer forcing precoder design is proposed.
## 4 Experimental Results and Discussion
### The Evolution of Frequency for Resultant Vibration in Beating Phenomenon
In this measurement method (e.g., the measurement of the combination frequency \(f_{c}\) of vibration displacement signal from Figure 7), the computer-aided detection of the time interval between each two consecutive zero-crossing moments (\(t_{z{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{   {               }}}}}}}}}}}}}}} \))) of a periodical signal is used. This time interval defines a semi-period \(T_{c}\)/2 = \(t_{z{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{{\rm{\rm{\rm{\rm{{ \rm{\rm{{\rm{        }}}}}}}}}}}}}}}}}}\)) as \(T_{c}\)/2 = 1/2\(f_{c}\), or a value \(f_{c}\) = 1/\(T_{c}\). When the result of multiplication of two successive displacements samples \(s_{i}\) and \(s_{i-1}\) (having the sampling times \(t_{i}\) and \(t_{i-1}\), with \(i>1\)) is negative or zero (\(s_{i}\):\(s_{i-1}<0\) or \(s_{i}\):\(s_{i-1}=0\)) a zero-crossing moment is detected (e.g., \(t_{z{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{ \rm{ \rm{  \rm{  \rm{       }}}}}}}}}}}}}}}}\)) and calculate as the abscissa of the intersection of a line segment defined by the points of coordinates (\(t_{i}\), \(s_{i}\)) and (\(t_{i-1}\),\(s_{i-1}\)) on the \(t\)-axis (as \(x\)-axis in Figure 8b). The main reason for frequency measurement error \(\varepsilon_{f}\
eq 0\) is a consequence of calculation errors for two successive zero-crossing moments \(\varepsilon_{j}\
eq 0\) (for \(t_{z{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{ \rm{ \rm{ \rm{      \rm{            }}}}}}}}}}}}}} \)) and \(\varepsilon_{  
A computer-aided calculus was performed for frequency measurement error \(\varepsilon_{f}\) of a harmonic simulated signal with frequency 17.383 Hz (the average value of the combination frequency \(f_{c}\)) during a semi-period. Here 10,000 different values of sampling time \(t_{1}\) (between 0 and \(\Delta t\), with \(\Delta t=200\)\(\upmu\)s, the same sampling interval as in Figures 3 and 7) and \(t_{2\,=\,}\Delta t-t_{1}\) (between \(\Delta t\) and 0) for the first two successive displacement samples involved in the calculus of the first zero-crossing moment \(t_{zc1}\) was used. Figure 16 describes the evolution of the frequency measurement error \(\varepsilon_{f}(t_{1})\). As Figure 16 clearly indicates, the frequency measurement error \(\varepsilon_{f}\) of the combination frequency \(f_{c}\) is variable and placed between \(-0.000935\) and \(+0.0014\) mHz. The result of the measured frequency of the simulated signal is \(17.383^{+1.4\ \upmu\mathrm{Hz}}_{-0.935\ \upmu\mathrm{Hz}}\) as a description of the accuracy measurement. Very similar limits for the \(\varepsilon_{f}\) error are calculated for a harmonic signal with frequency \(f_{1}\). If the value of the frequency \(f_{c}=1/T_{c}\) used in simulation accomplishes the condition \(T_{c}=h\Delta t\) (with \(h\) being an integer), then \(\varepsilon_{f}=0\) for any value \(t_{1}=0\div\Delta t\).
## 3 Multipath Parameter Estimate Algorithm
### Multipath Parameter Estimation
Then for a given false alarm probability, the corresponding threshold is also determined. One of the benefits of such process is we can set the threshold without the knowledge of the exact mathematical expression of the probability distribution. In this paper, as the main purpose is not at multipath detection but parameter estimation, we employ a simplified way to determine the presence of multipath as well as LOS. When to estimate the code delay of LOS in the multipath environment, we just need to search for the impulse with the largest amplitude basing on the fact that reflected signals must be weaker than the LOS signal. However, when to estimate the parameters of all multipath signals, we need to set the threshold with a pre-set false alarm probability just as described in the previous paragraph to find all impulses that excess the threshold. Then, the first impulse corresponds to the LOS and the other impulses represent the reflected signals. Thus in this way, we can estimate the parameters of all paths without need of a-priori assumption about the number of extra paths like MEDLL method.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 4 Results and Discussion
### Setup and Implementation Details
#### 4.2.2 Implementation Details  
We set the input length to 8 for each task. Thus, for the prediction task, the next time step of the time-series telemetry data was predicted based on the preceding eight steps. The model parameters are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Instances** & **Dimension** & **Labeled Anomalies** \\ \hline All & 0.9994 & 8 & 20,125 \\ Train & 491,343 & 8 & 0 \\ Test & 126,062 & 8 & 20,125 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed dataset statistics.  
During the experiment, the contamination proportions in the training data were set to 0.005 and 0.002. In each task, the errors between the true and predicted values were ranked, and the error value in the contamination percentile was used as the threshold (\(THR\)). For test sample \(S\), the prediction/reconstruction error was taken as \(Err(S)\). If \(Err(S)\) exceeded or was equal to \(THR\), \(S\) was identified as abnormal; otherwise, it was normal.
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 4 Discussion and Perspectives  
The use of bacteria in cancer therapy is often overlooked, although there is great evidence that this kind of treatment represents a promising chance to cure patients. In fact, bacteria and their different compounds can act as a double-edged sword when it comes to CRC, since specific species have been demonstrated as cancer-stimulating and triggering agents, whereby other bacterial strains show highly selective anti-cancer properties without cytotoxicity to vital tissue.  
Moreover, many of these bacterial agents have only been investigated in pre-clinical trials and detailed information about their toxins and metabolites is still limited, thus further research on their mode of action and properties in general is needed. In addition, the risk of uncontrollable complications of therapeutic bacteriotherapy due to infections represents another limitation for bacterotherapy to reach full acceptance in CRC treatment. However, several attempts have been made to overcome these issues, for example, by attenuating or even eradicating toxic compounds of bacteria as well as approaches to modify specific strains to reduce the risk in treatment application. Besides using bacterial agents or their toxins themselves in CRC therapy, specific bacteria have also been demonstrated to act as potential drug carriers due to their unique chemical characteristics such as low molecular weight and hydrophobicity together with their tumor- and metastasis targeting properties, while showing less side effects than conventional cancer treatment. Additionally, bacteria are not directly coupled to the specific extracellular or intracellular receptors so that bacterial anti-cancer peptides are able to prevent various resistance processes [169]. As another downside of bacterotherapy the short half-life of bacterial peptides has to be mentioned, displaying a major problem in the application. However, their chemical modifications have already been worked on, such as the substitution of D-amino acids, cyclization or the replacement of labile amino acids among other methods to improve the half-life and stability to make them more efficient for therapy [170; 171; 172].  
Altogether, it is of great importance to understand that bacteria and bacterial agents in application as well as the composition of our microbiome is a matter of great complexity and can hardly be considered separately. Therefore, better understanding of bacterial interactions, their metabolism in context of drug administration as well as effects of our microbiome are necessary and further use of artificial intelligence and machine learning is needed to develop customized high-efficiency therapy for individuals suffering from CRC.
# Inclence of Chronic Pain following Oral Soft Tissue Biopsies: A Retrospective Study
## 3 Results  
In total, 695 patient records were examined: 392 (56%) were women, and 303 (43%) were men. The median age was 59 \(\pm\) 2.0 years. The most common biopsy area was the buccal mucosa, the complete list of sites and frequencies can be seen in Table 1.  
Histopathological analysis identified 64 lesions, both malignant and non-malignant, stratified into eight categories (Figure 1). The majority were reactive lesions (37.3%), followed by non-malignant tumors (21%), and immune-related/infectious lesions (18%).  
The most common histological diagnoses were fibroma (220, 31.8%), lichen planus (88, 12%), mucoele (87, 12.5%), squamous cell carcinoma (35, 5%), pemphigus vulgaris (26, 3.7%), pyogenic granuloma (19, 2.7%), and papilloma (16, 2.6%); see Supplementary Material for the complete list (Table S1).  
Four patients (out of 695) reported at least 3 months of post-biopsy pain. The pain was mainly episodic in nature, and there was no need for persistent analgesics. The histopathological diagnosis, biopsy location and other clinically relevant information are detailed in Table 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Patient 1** & **Patient 2** & **Patient 3** & **Patient 4** \\ \hline Age (years) & 36 & 43 & 81 & 76 \\ \hline Gender & Female & Female & Female & Female \\ \hline Histopathological diagnosis & Fibroma & Venous malformation & Lichen planus & Lichen planus \\ \hline Location of the biopsy & Gingiva & Lower lip & Left buccal mucosa & Tongue \\ \hline Timing of pain after biopsy & Immediate & Immediate & Immediate & Immediate \\ \hline Constant or episodic pain & Episodic & Episodic & Episodic & Episodic \\ \hline Intensity of pain & Mild & Mild & Mild & Mild \\ \hline Duration of pain after biopsy & 1 year & 6 months & 3 years & 3 years \\ \hline \multirow{2}{*}{Physical examination} & On the top of the papilla of tooth 42 slight hyperplasia & Residual internal scar & \multirow{2}{*}{Normal} & \multirow{2}{*}{Normal} \\  & 2–3 mm, sensitive area & & 3 mm, sensitive to touch & \\ \hline Need for analgesics & No & No & No & No \\ \hline \multirow{2}{*}{Recommendation for treatment} & \multirow{2}{*}{Maintaining oral hygiene} & No intervention & No intervention & \multirow{2}{*}{Maintaining oral hygiene} \\  & & recommended & recommended & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Medical records and follow-up summary of patients with pain lasting Over 3 months.  
Figure 1: Histological diagnosis of biopsies divided by groups.
## 3 Methodology
### Trajectory Determination
#### 3.2.3 Sigma Point Kalman Smoother  
To further improve the accuracy of the system, a smoothing algorithm is used. By applying a smoothing algorithm, an optimal solution is found for the position and orientation of the LiDAR system based on the entire set of flight observations. The smoothing algorithm involves running two independent filters forward and backward in time. The optimal state estimate, for each epoch, is then found by optimally combining the forward and backward estimates and their error covariances. It is well known that these smoothed estimates, which now incorporate all measurements, provide a significantly improved estimate of the vehicles state, see [PERSON] [23] for an example.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 3 Results
### Temporal and Spatial Variation of Global GPP and NPP
Variation in global total GPP and NPP for all vegetated land cover types is demonstrated in Figure 7. Generally, variations in GPP and NPP from 2004 to 2008 were less than those from 2008 to 2012. The largest variation occurred in the EBF, in which GPP decreased 1.22 Pg C from 2004 to 2008 and increased 3.24 Pg C from 2008 to 2012. The next largest increases were seen in MF, OSH, woody savannas (WSAV), savannas (SAV) and GRA, GPP increased about 1 Pg C, and NPP increased about 0.6 Pg C from 2008 to 2012. The lowest variations were in DNF, DBF, close shrub-land (CSH), CRO, artificial land (AL), cropland/natural vegetation mosaic (C/NV) and BL, in which variations of GPP and NPP were less than 0.2 Pg C. Considering that some of that variability can be explained by regional patterns, we also studied the mean GPP and NPP for different biomes, as well as the variation in GPP and NPP in different biomes (See Text S1 in Supplementary Materials). Figure 3: Global 1 km GPP and NPP in 2004, 2008 and 2012: (**a**) global GPP in 2004; (**b**) global NPP in 2004; (**c**) global GPP in 2008; (**d**) global NPP in 2008; (**e**) global GPP in 2012; (**f**) global NPP in 2012. Figure 4: Variations in global GPP and NPP from 2004 to 2012: (**a**) variation in GPP from 2004 to 2008; (**b**) variation in NPP from 2004 to 2008; (**c**) variation in GPP from 2008 to 2012; (**d**) variation in NPP from 2008 to 2012. Figure 5: Global GPP and NPP in 2004, 2008 and 2012 estimated using Global LAnd Surface Satellite (GLASS) data. Figure 6: Global mean and standard deviations of GPP and NPP for all vegetated land cover types: (**a**) global mean and standard deviations of GPP; (**b**) global mean and standard deviations of NPP.