esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Issue Negotiation Protocol with Pre-Domain Narrowing
## 4 Negotiation Protocol with Pre-Domain Narrowing
### Naive Agent Strategies for the Negotiation Domain Space Narrowing
#### 4.4.1 Strategy for Narrowing Issues Using the Simultaneous Submission Method  
When using the simultaneous submission method for narrowing issues, agents submit a list of issues that do not need to be negotiated from their perspective to the mediator. The offer related to a submitted issue is selected by the other agent or mediator. Therefore, the agreement on the submitted issues is probably disadvantageous to the submitter. As a naive strategy for the simultaneous submission method, we consider a method of submitting a list of issues that are unlikely to disadvantage the submitter. Here, the minimum utility \(U_{min}\) that the agent can concede will be used as a parameter, and narrowing down is performed within the range where the acquired utility is expected to be \(U_{min}\) or greater.  
For a given issue \(I_{i}\), we assume that the probability of each option being selected follows a uniform distribution when deciding an alternative of agreement during the main negotiation; the expected utility is \(w_{i}\times\dfrac{1}{k_{i}}\sum\limits_{j=1}^{k_{i}}eval(v_{j}^{i})\). Moreover, if an agent submits an issue and the option that is most unfavorable to the agent is selected, the utility is \(w_{i}\times\min\limits_{j}eval(v_{j}^{i})\). It can be considered that the smaller the difference between these values, the less likely it is that the agent will be disadvantaged. Therefore, we rearrange the issues as \(I_{0_{1}}\), \(I_{0_{2}}\), \(\ldots\), \(I_{0_{n}}\) in ascending order of the differences.  
Next, from these issues, the number of issues \(m\) to be submitted is decided. If \(m\) issues are decided upon and submitted according to the order above, the utilities expected to be obtained are as follows:  
\[U_{expected}=\sum\limits_{i=1}^{m}\left(w_{o_{i}}\times\min\limits_{j}eval(v_{ j}^{o_{i}})\right)+\sum\limits_{i=m+1}^{n}\left(w_{o_{i}}\times\dfrac{1}{k_{o_{i}}} \sum\limits_{j=1}^{k_{o_{i}}}eval(v_{j}^{o_{i}})\right) \tag{3}\]  
The maximum \(m\) within the range where the expected utilities do not fall below \(U_{min}\) is determined, and the list of issues \(I_{0_{1}},I_{0_{2}},\ldots,I_{0_{m}}\) is submitted to the mediator.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
###### Abstract  
Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel- and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method _vs._ using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO.  
mobile positioning; commercial vehicle operation data; cellular network; cloud computing +
Footnote †: journal: Geo-Inform
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 3 Improving 3D Solid FE Solutions
### Tool Jig Problem  
A tool jig problem is considered as shown in Figure 13. The structure is fixed along its left surface at \(y=0\) and subjected to a distributed force \(p=10\)\(\mathrm{N/m^{2}}\) along its upper surface marked in red. [PERSON]'s modulus \(E=2.0\times 10^{5}\)\(\mathrm{N/m^{2}}\) and Poisson's ratio \(\
u=0.3\) are given. The coarse and fine meshes shown in Figure 14 are obtained using both HEX8 and PRI6 elements. The coarse mesh consists of 336 HEX8 and 12 PRI6 elements, while the fine mesh consists of 2450 HEX8 and 8 PRI6 elements. The reference solution was obtained using 27-node hexahedral elements with the fine mesh.  
The von Mises stress distributions for the tool jig problem with coarse and fine meshes are given in Figure 15. Moreover, the order of cover functions obtained from the adaptive procedure at each node is depicted. Tables 4 and 5 provide the relative error in the maximum von Mises stress and a summary of the adaptive solution procedure, respectively. The results of the adaptive enrichment scheme show a significant improvement compared to those without cover functions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{2}{c}{**Strain Energy**} & \multicolumn{2}{c}{**von Mises Stress**} \\ \cline{2-5}  & **Standard** & **Adaptive** & **Standard** & **Adaptive** \\  & **Solution** & **Solution** & **Solution** & **Solution** \\ \hline Coarse mesh & 19.06\% & 9.84\% & 25.40\% & 9.65\% \\ Fine mesh & 7.44\% & 4.27\% & 12.72\% & 6.18\% \\ \hline Reference solution & \(E_{ref}=6.525\) Nm & \(\tau_{ref}=3.955\times 10^{1}\)\(\mathrm{N/m^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative errors in strain energy (\(e_{E}\)) and maximum von Mises stress (\(e_{\tau}\)) for the gear problem using coarse and fine meshes.  
Figure 13: Description of the tool jig problem (\(E=2.0\times 10^{5}\)\(\mathrm{N/m^{2}}\), \(\
u=0.3\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{2}{c}{**DOFs**} & \multicolumn{3}{c}{**Number of Nodes**} \\ \cline{2-7}  & **Standard** & **Adaptive** & **\multirow{2}{*}{**\(\alpha\)**} & **None** & **Linear** & **Quadratic** \\  & **Solution** & **Solution** & & & **Cover** & **Cover** & **Cover** \\ \hline Coarse mesh & 7308 & 17,469 & 0.22 & 2021 & 293 & 293 \\ Fine mesh & 24,579 & 47,637 & 0.15 & 7396 & 646 & 646 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Supplementary information on the adaptive enrichment scheme for the gear problem.  
Figure 14: The numbers of elements and nodes for (**a**) coarse and (**b**) fine meshes in the tool jig problem.  
Figure 15: von Mises stress (\(\tau_{vM}\)) distributions for the tool jig problem. Solutions are obtained by using coarse and fine meshes.
## 5 Experiments and Results
### Vertical Imaging Mode  
The vertical imaging mode was used for photogrammetry mapping. The LOS of the area camera lied along the local geodetic vertical. Therefore, the target roll angle and target pitch angle were 0\({}^{\circ}\), \(X_{5}\) was parallel with the aircraft velocity's direction, and the along-fight overlap rate of two successive images should have been greater than 56%. The imaging cycle \(t_{p}\) was calculated using Equation (1) in real time, and the time sequence in each imaging cycle was adjusted to meet the overlap rate requirement. During the preparation period, \(\varphi\), \(\omega\), and \(\kappa\) were calculated in real time using Equations (6)-(8), respectively. The servo control module swiftly drove the three axes of the platform to the given gimbal angles. During the photographing period, \(\omega_{F}\) was calculated using Equation (14) and set as the given value for the velocity loop of the pitch axis to compensate for the image motion. The maximum magnitude of \(v_{A_{E}}^{S}\) was 0.49 mm/s according to Equation (20).  
Figure 12: Three-axis ISP and iXU-RS1000 camera.  
Figure 13: Three-axis ISP loaded in the payload chamber of an unmanned aircraft.  
The maximum image motion was 2.3 \(\upmu\)m during the flight tests. Therefore, the exposure duration should have been no longer than 4.7 ms. Figure 14 shows the mosaic image series of six successive images in the vertical imaging mode. The circled number on each image in Figure 14 represents the imaging order. The enlarged images in the square imply that the image quality was excellent and the image motion was well compensated. The image attitude deviations of thirty successive images are shown in Figure 15. HD in Figure 15 stands for the heading deviation, which is the deviation from \(X_{S}\) to the flight direction. The image's azimuth or roll deviation was less than 0.1\({}^{\circ}\), and the image's pitch deviation was less than 0.33\({}^{\circ}\). The imaging attitude in the vertical imaging mode was accurately controlled, and the overlap rate was greater than 65%.
## Materials
### GPP Datasets
#### 2.2.6 Fluxcom  
FLUXCOM is an initiative formed through the collaboration of various experts, aiming to understand uncertainties in empirical scaling and to present global GPP values based on machine learning [69]. In FLUXCOM, a variety of machine learning-based regression tools, such as model tree ensembles, multiple adaptive regression splines, artificial neural networks, and kernel methods, are used. This study aims to estimate CO\({}_{2}\) and energy fluxes using two different scaling strategies: (1) 8-day average fluxes based solely on remotely sensed data and (2) daily average fluxes based on remotely sensed and meteorological data. Machine learning (ML) approaches have been applied and extensively cross-validated with flux towers for both setups, and it is important to note that FLUXCOM was designed to not have trends in the \"RS + METEO\" products used here [70]. For the calibration of GPP values in the study, FLUXNET GPP values were used. The dataset was obtained from [[http://www.fluxdata.org](http://www.fluxdata.org)]([http://www.fluxdata.org](http://www.fluxdata.org)) (accessed on 11 December 2023).  
Due to the varying lengths of these time series across datasets, the start date of the latest commencing dataset, GOSIF, was chosen as the baseline start date. Similarly, the end date of the earliest finishing dataset, PMLV2, was used as the overall end date to enable more accurate comparisons between the datasets.
## 6 Discussion
### Environmental Factors Affecting SIF  
Finally, this paper aims to draw attention to some aspects that are typically ignored or considered as secondary, such as the O\({}_{2}\) transmittance dependency on the environmental conditions, e.g., \(T\) and \(p\). This dependency, analysed in this work within a theoretical framework and following the approximation of ([PERSON] et al., 2016), may imply the detection of trends in the retrieved SIF that are not actually related to the emitted signal but are caused by a failure to apply a proper oxygen transmittance compensation. The impact of seasonal changes in \(T\),\(p\) was more significant for the O\({}_{2}\)-A than for the O\({}_{2}\)-B spectral region. Therefore, for proximal sensing scenarios, attention should be paid to the O\({}_{2}\)-A absorption lines. Results indicate that summer-winter transitions (i.e., in this case with \(T\) and \(p\) variations of \(\sim\)40 \({}^{\circ}\)C and \(\sim\) 50 mbar corresponding to measurements taken at a latitude of \(\sim\)60 \({}^{\circ}\)N) can produce changes in radiance units as large as 0.25 [mW/m\({}^{2}\)/sr/nm], which can represent a significant fraction of the SIF signal. However, implications for other regions of the planet such as the tropics do not become so critical since no abrupt \(T\) and \(p\) variations are expected in these regions during winter-summer transitions.  
Although focus must be especially kept on long-temporal data series, when having the possibility of measuring a non-fluorescence target, this can provide an adequate validation strategy. The validation can be achieved by computing the reflectance spectrum at surface level by using radiance measured at the top of the tower, corrected by modelling the oxygen transmittance using the measured \(T\) and \(p\) conditions. If the measured \(p\) and \(T\) conditions are accurately estimated, no peaks should be observed in the derived surface reflectance, and therefore this test can be used as a kind of validation step. This could be the case, for instance, of rotating systems that in certain angles monitor non-vegetated (and therefore non-fluorescence) areas.
# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 3 Our Approach for Fall Detection  
In our proposed method, a new feature named torso angle is adopted. This feature together with the centroid height and their motion characteristics form a descriptor for human fall representation, called the human torso motion model (HTMM). Different from machine learning and deep learning methods, HTMM is a threshold-based approach for fall detection with high time efficiency because of the calculation of only the changing rates of the torso angle and centroid height. Before we elaborate the model, some technical terms will be detailed first in the following.
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Non-Thermal Ablative Therapies
#### 2.2.3 Transarterial Chemoembolization  
Transarterial chemoembolization (TACE) is the standard treatment for patients with BCLC stage B and is one of the most widespread and studied therapeutic options for HCC [86]. The method implies a transarterial approach to the liver nodule under fluoroscopic guidance. Conventional TACE (cTACE) uses a lipiodol drug-charged emulsion followed by an embolizing substance (Figure 7), while a separate technique uses drug-eluting beads (DEB) that release a chemotherapeutic agent over longer periods of time [87] (Figure 8). There are numerous reports of outcomes following TACE, and it appears that DEB-TACE provides a better tumor response than cTACE; the complete response, disease stability, and mortality are overall similar between the two techniques [88].  
TACE induces necrosis through a combination of the cytotoxic effect of the injected drugs and the ischemia secondary to the arterial occlusion. The necrosis seems to be greater in supraselective TACE compared to lobar TACE and is directly proportional to the tumor diameter [89]. However, smaller nodules are often hypovascular, a factor negatively affecting the effectiveness of the method [89]. The obtained necrosis is coagulative and is associated with moderate liquefaction and fluid accumulation; infiltration with immune cells may appear in the periphery and areas of cystic changes may be seen after the  
Figure 7: Unenhanced CT scan of a patient with HCC in the right liver lobe, 1 day after cTACE (**a**), and MRI scan of the same patient 5 weeks later (T1 weighted dual echo: “in phase”—(**b**) “out of phase”—(**c**)). The lipiodol used in conventional TACE is hyperdense on CT (**a**), and has signal intensity similar to fat on T1 dual-echo images (arrow)—“in phase” high signal intensity and “out of phase” signal drop.  
treatment of larger nodules [90]. The presence of liquefactive necrosis may favor bacterial infection, therefore antibiotics may be required in larger tumors [91].
## 4 Discussion  
There is considerable growing interest in OMA, due to the modal dynamical properties that can be estimated from the response vibration measurements data. Modal parameters  
Figure 11: Typical generated FRF of the gearbox for OMA at 3000 rpm.  
Figure 12: Comparison of the gearbox’s FRFs, with eight modes at varying speeds (1800, 2400, and 3000 rpm).  
Figure 10: Comparison of the induction motor’s FRFs, with five modes at varying speeds (1800, 2400, and 3000 rpm).  
play an important role in structural health monitoring and fault detection. In any case, it will be impossible to apply standard EMA for ambient vibration testing. It is well-known that EMA is carried out under complete machinery shut down. EMA measurement requirements include that the measured object has to be suspended on soft springs to reduce the initial conditions' effects.  
OMA's major advantages are: the testing procedure can be affordable, cheap, and easy to measure directly on the structure's surface; testing does not interfere with the machine or structure operation process; the method can be used for large and heavy structures, there is no need for whiskers; and there is no need to lift the measurement objects up or hang them up on soft springs.  
The OMA also assumes that the structure excitation is on the broadband frequency; however, it can also contain discrete components, such as shaft speed harmonics and gear mesh components. Discrete frequency components can disturb the operation of OMA algorithms. It is quite important to perform a pre-processing of the response signals, in order to remove unwanted excitation components before the application of OMA.  
The main objective of our research paper was to determine the structure natural frequencies, based on two of most popular methods, RDT [1, 4, 11, 18] and EITM [3]. The methodology was applied in a steel bar, induction motor, and gearbox. Special attention was paid to closely spaced modes. The CFE and windowing techniques were also applied, similar to what was used in paper [15] to reduce OMA limitations. However, in this case, the filtering technique was used to eliminate the harmonic effects. Different frequency bands were also used as a pre-processing technique to reduce noise. Extensive data tests were performed for the generation of FRFs. The methodology applied in this paper could be an interesting alternative for OMA for health monitoring, fault detection, and machine diagnostics.
## 2 Materials and Methods
### Hilbert Spectrum  
After the Hilbert transform is performed on each IMF component, the given time series, \(x(t)\), is expressed as follows [33]:  
\[x(t)=\sum_{j=1}^{n}a_{j}(t)\ exp\bigg{(}i\int\omega_{j}(t)dt\bigg{)} \tag{3}\]  
where \(n\) denotes the mode number, and \(a_{j}(t)\) and \(\omega_{j}(t)\) denote the instantaneous amplitude and frequency of \(j\)-mode IMF, respectively. Since the amplitude, \(a_{j}(t)\), can be interpreted as the Hilbert spectrum, \(H(\omega,t)\), we can define the marginal spectrum, \(h(\omega)\), as the following formula [33],  
\[h(\omega)=\int_{0}^{T}H(\omega,t)dt \tag{4}\]  
In addition, the instantaneous energy density level [33], IE, is defined as  
\[IE(t)=\int_{\omega}H^{2}(\omega,t)d\omega \tag{5}\]  
Using the IE of each SSTA signal, we examine the energy fluctuation over time, as well as the spatial distribution pattern of all SSTA signals in the EJS at a specific time.  
Figure 2 shows the marginal spectrum, \(h(\omega)\), of IMFs for all SSTA databases. First, we can see that a dominant timescale increases as the mode number increases. For low-mode IMFs (1 through 4), the marginal spectrum value is low and a clear peaked period is observed, while higher mode IMFs have a high spectrum value and a wide-width period. Two modes of IMF-6 and IMF-7 have a nearly same peaked timescale, corresponding to an annual period, with different skewed directions. The former is skewed toward a shorter scale, while the other is skewed toward a longer scale, indicating any intra- and inter-wave frequency modulations [33].  
Figure 2: Marginal Hilbert spectra of an ensemble of all SSTA IMFs in the form of mean \(\pm\) STD.  
Compared to orthogonal modes via the Fourier transform, IMFs usually have intra- and inter-wave frequency modulations, as well as amplitude modulations. Thus, it is not plausible that any IMF preserves its peak frequency over the whole duration. However, in this study, the peaked timescale is used as a representative scale for its corresponding IMF, along with considering the frequency modulation effect.  
The peaked scales of all IMFs corresponding to a singular SSTA series are summarized in Table 1, where the empirically observed scales of the SSTA variability in previous studies are also included for comparison. As mentioned in the Introduction section, the smaller scales correspond to local wind forcing and synoptic atmospheric scales [17; 18; 19; 20], while the longer scales are mainly attributed to advection timescales as well as various climate factors [3; 4; 11; 12]. In the following analysis, the characteristics of spatial pattern and temporal evolution of SSTA variability are examined in terms of IMF-based IE and networks for weekly to decadal timescales.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Challenges in ITS Sensing
#### 4.3.3 Challenge 3: Sensing in Extreme Cases  
ITS sensing tasks that seem simple could become extraordinarily complicated or unreliable in extreme cases, such as during adverse weather, due to occlusion, and at nighttime. A typical example is video sensing, which is sensitive to lighting changes, shadow, reflection, and so forth. In smart parking surveillance, a recent study showed that video-based detectors performed more reliably indoars than outdoors due to extreme lighting conditions and adverse weather [4]. Due to low-lighting conditions, even the cutting-edge video-based ADAS products on the market are not recommended for operation at night [239]. The LiDAR sensor is one of the most reliable sensors for ITS; however, LiDAR sensing performance downgrades in rainy and snowy weather, and it is also sensitive to objects with reflective surfaces. GPS sensors experience signal obstruction due to surrounding buildings, trees, tunnels, mountains, and even human bodies. Therefore, GPS sensors work well in open areas but not in areas where obstructions are unavoidable, such as downtown.  
In ITS, especially in automated vehicle testing, extreme cases can also refer to corner cases that an automated and intelligent vehicle has not encountered before. For example, a pedestrian crossing the freeway at night may not be a common case that is thoroughly covered in the database, so a vehicle might not understand the sensing results enough to proceed confidently; therefore, it would cause uncertainty in the real-time decision making. Some corner cases may be created by attackers. Adding noise that is unnoticeable byhuman eyes to a traffic sign image could result in a missed detection of the sign [240]; these adversarial examples threaten the security and robustness of ITS sensing. Corner case detection appears to be one of the hurdles that slow down the pace towards L-5 autonomous driving. The first question is: how does a vehicle know when it encounters a corner case? The second question is: how should it handle the unforeseen situation? We expect that corner case handling will not only be an issue for the automated vehicle but is also faced by the broad ITS sensing components.  
There have been research studies that focused on addressing extreme case challenges. [PERSON] et al. [241] developed a domain adaptation method that used UAV sensing data from daytime to train detectors for traffic sensing at nighttime. The transfer learning method is a promising direction to address extreme cases in sensing. With edge computing, the machine is expected to be able to collect onsite data and improve the sensing functions over time. A particular edge device at one certain location could overfit itself for improved sensing performance at that certain location, though overfitting is not good in traditional machine learning.