esa-sceva/satcom-chunk-collection huggingface dataset:
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 2 Maritime Scenario and Problem Statement  
On their journey across the Baltic Sea from Helsinki/Vuosaari to Tallinn/Muuga, ferries encounter significant variations in 4G and 5G coverage that prevent them from ensuring continuous onboard connectivity.  
Close to land, the terrestrial base stations in the ports and along the coast are able to provide sufficient bandwidth. However, as is quantitatively assessed in the following, with increasing the distance to the shore, the signal strength decreases. Thus, as already discussed in the introduction, alternative connectivity solutions enabling better coverage and quality of service (QoS), such as satellites and/or multi-hop (vessel to vessel) relays, are considered in this paper.  
The following subsection presents the current 4G and 5G coverage status in the Baltic Sea. To perform the measurements, two cellular base stations currently deployed in the port of Vuosaari, operating at 3.5 GHz and 700 MHz, were used for the measurements. For the coverage measurements, based on the received signal strength at the end-device, a Rohde&Schwarz FR4 Freerider 4 backpack system was used, as shown in Figure 1. This equipment provides a complete and compact drive test system that can be temporarily installed inside a vehicle/vessel, significantly reducing the setup time for measurement campaigns.
## Materials and Methods  
### Patients and Tumor Samples  
Seventy-one patients with histologically confirmed diagnosis of invasive breast cancer and complete data regarding the receptor status and molecular subtype were retrieved from archives of King Abdullah University Hospital (KAUH) through the period of 2004-2014. All included patients underwent mastectomy or wide local excision with or without chemotherapy, according to tumor board recommendations. Relevant demographic and anthropometric data were collected from medical records and the electronic database. Body mass index (BMI) was calculated using the standard method as weight in kilograms divided by the height squared in meters. Based on World Health Organization (WHO) definition for obesity, patients were classified as underweight (BMI \(<\) 18.5 kg/m\({}^{2}\)), normal (BMI 18.5-24.99 kg/m\({}^{2}\)), overweight (BMI 25.0-29.99 kg/m\({}^{2}\)), and obese (BMI \(\geq\) 30.0 kg/m\({}^{2}\)) [25].  
Pathological data were retrieved from pathology reports issued by the Pathology Department at KAUH at the time of diagnosis. Pathological data included details of tumor size, grade, histological subtype, ipsilateral axillary lymph node status, and presence/absence of lymphovascular invasion (LVI). In addition, pathology reports provided details for the expression status of estrogen receptor (ER), progesterone receptor (PR), and Human Epidermal Growth Factor Receptor 2 (HER2/neu). For HER2, immunohistochemistry (IHC) scores of 0 or +1 were considered negative while a score of +3 indicated positive staining. For equivocal results of +2 score, fluorescence in situ hybridization (FISH) analysis positive for gene amplification was positive for HER2 expression. ER-positive patients received hormonal treatment and patients overexpressing HER2 received trastuzumab for one year.  
The clinicopathological stage of breast cancer was obtained according to the tumor-node-metastasis (TNM) cancer staging systems of the American Joint Committee on Cancer (AJCC) [26]. The grade of carcinoma was determined based on the Nottingham Combined Histologic Grade system [27]. Accordingly, tumors were classified into grade I (well-differentiated/low grade), grade II (moderately-differentiated/intermediate grade), and grade III (poorly-differentiated/high grade) carcinomas. Breast cancer was further classified into subtypes based on the expression of hormone receptors and HER2 status [28; 29] including luminal (ER+ and/or PR+, HER2+/-), HER2-positive (ER-, PR-, HER2+), and triple-negative (ER-, PR-, HER2-).  
The Institutional Review Board (IRB) committee of JUST and KAUH (Research number 33/80/2014) approved the study.
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.1 Feature Learning and Representation  
Deep learning methods, particularly Convolutional Neural Networks (CNNs), excel at learning hierarchical features directly from raw data. They automatically extract intricate patterns and representations, allowing for end-to-end training and robust feature learning [1,12].  
DeepSegDDPM combines CNNs with DDPM, offering a unique advantage. DDPM contributes to the denoising of input images during pre-processing, allowing subsequent deep learning layers to focus on learning relevant features without being hindered by noise [75,76].
# Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease
## 1 Introduction
Most interesting, over the last decade Raman spectroscopy has proved to be a versatile tool in clinical diagnostics too, applied on tissues in order to detect a variety of diseases ranging from cancer [26] to infectious [27], neurodegenerative diseases [28] and in identification of cells [29]. This is because any altered biochemistry in the body that is specific to a particular disease and that will precede macroscopic tissue changes [30; 31] will be reflected in a Raman spectrum. In particular, many inflammatory conditions have been investigated by Raman spectroscopy. For example, an endoscope-coupled Raman probe was used in vivo to investigate the colon mucosal composition in patients suffering from ulcerative colitis, for which a decrease in the phosphatidylinositol (720 cm\({}^{-1}\)) and total lipids (1303 cm\({}^{-1}\)) was detected [32]. In addition, colonoscopy-coupled fiber-optic probe-based Raman spectroscopy was applied in vivo for the diagnosis, in real time and in a nondestructive way, to inflammatory bowel disease, i.e., ulcerative colitis and [PERSON]'s disease [33]. With the aim of identifying a diagnostic procedural path for inflammatory bowel diseases, in particular CD and UC, that takes into consideration _only_ a physical investigation technique, a noninvasive methodology has therefore been developed based on the analysis of the stool protein extract of patients, using Raman spectroscopy. As is well known, the evaluation of diagnostic tests is a matter of concern in modern medicine not only for confirming the presence of disease but also to rule out the disease in healthy subjects [34]. In diagnostic tests with dichotomous outcome (positive/negative test results), the conventional approach of the diagnostic test evaluation uses sensitivity and specificity as measures of the accuracy of the test in comparison with gold standard status [35]. Here, the reliability of the proposed innovative methodology was evaluated by conducting statistical analysis by using, in particular, the receiver operating characteristic (ROC) curve and the Youden index (_J_), with its associated cutoff-point.
## 3 Data Preparation
### Data Preprocessing  
#### 3.3.1 Data Smoothing  
Given the large oscillations and noise observed in the raw on-site data, it becomes imperative to undertake a smoothing process to mitigate the extent of fluctuation. This process makes the data more stable and conducive to later analysis and modeling. Exponential smoothing is a widely used method for time-series forecasting that assigns exponentially decreasing weights to past observations. It assumes that recent observations bear more relevance in the prediction of future values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Control Factors** & **Relevance with Gas Production** \\ \hline Geological & \begin{tabular}{c} Formation thickness (ft) \\ True vertical depth (ft) \\ \end{tabular} & \begin{tabular}{c} affects the volume of gas available for production. \\ \end{tabular} \\ \hline Fracture & \begin{tabular}{c} Fracture half-length (ft) \\ Fracture height (ft) \\ \end{tabular} & \begin{tabular}{c} A longer and taller fracture allows for a larger \\ contact area with enhanced gas production. \\ \end{tabular} \\ \hline Fracture & \
\begin{tabular}{c} Lateral length (ft) \\ \end{tabular} &
\begin{tabular}{c} Longer lateral length increases surface area \\ available for gas production. \\ A larger volume of fluid can create more extensive \\ and interconnected fractures, resulting in \\ potentially higher gas production. \\ Increasing the amount of proppant used can \\ enhance fracture conductivity, allowing for \\ improved gas production. \\ Higher injection rates can lead to the creation of \\ longer and more extensive fractures, increasing the \\ surface area available for gas flow. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The physical constraints for shale gas production.  
Figure 6: Production profiles of the two wells.  
The exponential smoothing method computes the forecasts by combining a weighted average derived from previous observations with a smoothing factor. The smoothing factor determines the weight given to the most recent observation and governs the pace at which the relevance of preceding observations wanes. This phenomenon is articulated through the subsequent formula [38]:  
\[\mathrm{y_{t}}=\alpha\mathrm{x_{t}}+(1-\alpha)\mathrm{y_{t-1}} \tag{11}\]  
where \(\mathrm{y_{t}}\) is the smoothing value at time (t), \(\mathrm{x_{t}}\) is the actual value at time t, \(\mathrm{y_{t-1}}\) is the smoothing value at time (t \(-\) 1), and \(\alpha\) is the smoothing factor ranging from 0 to 1. Figure 7 shows a comparison of the daily gas production of well MIP-3H pre- and post-smoothing. The smoothed data will be utilized for the subsequent study.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 3 Initial Design Decisions for Implementing UW-ASN
As mentioned before, real-time messages should be received before their deadlines; therefore, managing the delays is a key design aspect in these networks. These delays have two major sources: one is originated in the time needed by a message to go from the source to the destination node, and the second is represented by the delay a message may have in each hop, since several messages may be in queue and some scheduling policy should be implemented. In the first case, the delay time is determined by the number of hops, the sound speed in the water, and the distance between the nodes. These are physical parameters that are independent of the implemented transmission policies. For determining the second delay case, we propose to use a deadline monotonic scheduling policy for which we compute the worst case delay. Based on it, we provide a feasibility test for real-time messages.
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 3 Results
### Navigation Test with Unobstructed GNSS  
In the experimental orchard area without anti-bird nets, the road between the rows of apple trees has no overhead obstructions. This condition allows for a strong satellite differential signal, enabling the autonomous navigation test for the orchard's robotic vehicle. Given the robust satellite differential signal, the system relies solely on the GNSS/INS system's positioning information for path tracking. The walking trajectory during navigation is shown in Figure 13, and the lateral deviation value of the real walking trajectory from the preset navigation path is shown in Figure 14. Due to the uneven ground of the orchard, and because the robotic vehicle used in the test is small and prone to side-slip, the lateral deviation value fluctuates greatly in the figure. The test indicates that the maximum path-tracking lateral error does not exceed 0.35 m, and the average path-tracking lateral error does not exceed 0.1 m. The standard deviation of the lateral error is 0.076 and the root mean square error (RMSE) is 0.016 m.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 6 Performance Evaluation
* Energy efficiency: Energy efficiency is computed as the ratio of the number of packets the sink receives by the total energy consumed by the protocol in the network. * Route Stability Ratio: Route stability ratio is the ratio of unsuccessful routes between each node in the network to the number of all possible routes. In Figures 13 and 14, MROR is compared to ROR to show the effect of utilizing the spatio-temporal opportunities provided by mobility in routing. In both cases, MROR was set at \(v=5\) m/s, and ROR was set at \(v=0\) m/s. From Figure 13, it can be observed that in the case of MROR, the throughput  
Figure 12: Mobility-induced spectrum availability in a cognitive radio sensor network (CRSN). initiation increases as the network node density increases. What is responsible for this is that as the number of nodes increases, network traffic emanating from the source increases, and thus, the network at the sink increases. However, the throughput is observed to be somewhat stable when the density is about 120. Likewise, when the PU activity increases, it results in a corresponding decrease in throughput, as channel access time has been reduced. Although the channel access time is reduced, MROR is still able to record a good throughput when it is compared to ROR, which does not consider the spatio-temporal opportunities provided by mobility. It can also be derived that the best operational network density for the operation of MROR is at the point of 120 because of the observed stability of the throughput at this point when compared to ROR. For clearer observance of this property, the goodput performance is presented in Figure 14.  
Figure 14 also compares the goodput performance of both MROR and ROR at various PU activities. Although both the network load and the PU adversely affect the final network goodput, it is observed that the PU activity has more effect than the network load. This is because MROR and ROR adopt the same forwarding strategy that considers instantaneous link quality reliability. In addition, the receiver contention prioritization scheme ensures the stability of the goodput as the load increases, since the number of eligible nodes within the priority region also increases as a result. Furthermore, from MROR, it can be also observed that mobility also increases the opportunity for quality links. However, as can also be observed, when the number of contending nodes around the priority regions increases, this also increases the probability of collision, thereby resulting in a decline in the goodput. Unlike in ROR, where the goodput remains stable at intervals within certain SU density levels, a continuous decline is observed in the case of MROR from an SU density level of 120 as a result of the effect of mobility and frequent changes of the position of the nodes with respect to the spatio-temporal characteristic. This is why this density level has been chosen as the best operating value for MROR. Figure 13: Throughput _vs._ number of network nodes.
# Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
## 5 Scalability Assessment  
In addition to qualitative experiments aimed at illustrating the use cases of the proposed approach, quantitative experiments have been carried out to assess the scale of solvable problems. For this purpose, an assessment of the problem-solving time of \(CP_{AP}\) in (66) and \(CP_{AR}\) in (67) was carried out for a different number of service teams, \(NW=1\ldots 5\), and different number of turbines, \(NI=5\ldots 12\). In these experiments, it was assumed that the setting time for the repair mission plan is equal to the sum of the setting time for the proactive plan (time to solve the problem \(CP_{AP}\) in (66)) and time to determine the reactive plan (time to solve the problem \(CP_{AR}\) in (67)). The experiments were conducted in an IBM ILOG environment (Intel Core i7-M4800 MQ 2.7 GHz and 32 GB RAM, Intel, Santa Clara, CA, USA).  
The results (see Table 5 and Figure 9) show that the solution time for the considered problem increases exponentially with the number of turbines \(NI\) and service teams \(NW\).  
The results of the experiments show that the proposed approach can be used to support decisions made in the online mode (i.e., \(<\)10 min) for a service area with no more than five service teams and eight WTs (Figure 9). The value of the adopted input data corresponds to the scale of situations encountered in practice, i.e., collection of serviced  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No. of Service Teams** & **No. of WTs** & **TC [s]** & **No. of Service Teams** & **No. of WTs** & **TC [s]** \\ \hline  
(>turbines in 1 working day: horizon \(H=8\) h, number of serviced WTs \(NI\leq 8\), and number of service groups \(W\leq 5\). This result means that the model can be used to support online service-mission planning for real wind farms.  
The conducted experiments show that the developed model can be used in the process of planning one-day service missions online (mission planning time \(<10\) min, mission execution time up to 8 h). In all experiments, it was assumed that only one transport vessel was used, which corresponds to situations encountered in practice. This does not mean, however, that in future research, it will not be possible to consider more complex situations in which the delivery and collection of service teams is carried out by a fleet of ships. The proposed reference model makes it possible to plan servicing in such situations; however, ensuring the ability to make decisions online requires the development of a new, more computationally efficient method for assessing acceptable scenarios.  
It is once again worth emphasizing that the greatest advantage of the developed model is its open structure. This allows for taking into account additional constraints that describe the individual characteristics of the considered WT. The use of declarative programming allows for additional constraints to be taken into account without affecting the time needed to obtain the solutions. This is especially important for the various practical applications of the developed model.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 1 Introduction
### C-ITS Field Studies  
The paper includes results of two field-level studies evaluating driver acceptance of in-vehicle information provided by C-ITS services.  
The EU H2020 project ESRIUM was a joint multinational effort to improve the safety and resource efficiency of road transport. The goal of the project was an accurate digital map of road surface damage and road wear, which enables stakeholders to proactively take resource-saving measures. Thus, routing recommendations in terms of lane changes or lane offsets can both reduce road wear and increase traffic safety. At the same time, preventive measures of this kind can avoid or delay any road maintenance measures, and artificial intelligence can be used to identify potential road damage at an early stage. In ESRIUM, the user acceptance of C-ITS services regarding lane change and lane offset recommendations was investigated in a field study with ten truck drivers.  
The impact assessment study, funded by the Austrian motorway operator ASFINAG, aimed to evaluate the effects of Day 1 C-ITS services deployed on the Austrian motorway  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Service** & **Use Case** & **Use Case Description** \\ \hline \multirow{2}{*}{(1) Road Works Warning} & \multirow{2}{*}{(1.1) Lane Closure} & Inform the driver about road works \\  & & ahead with a closed lane and how to \\  & & drive (pass left or pass right) \\ \hline \multirow{2}{*}{(2) Hazardous Location Warning} & \multirow{2}{*}{(2.1) Traffic jam ahead} & Warning about a traffic jam ahead \\  & & \\  & (2.2) Emergency of Rescue/Recovery & Warning about an emergency vehicle on \\  & Vehicle in Intervention & the road for example due to an accident \\ \hline \multirow{2}{*}{(3) In-vehicle signage} & \multirow{2}{*}{(3.1) Traffic Signage} & Informing about the current speed limit \\  & & \\ \multirow{2}{*}{(4) Signalized Intersection} & \multirow{2}{*}{(4.1) Signal Phase and Timing} & Informing about the current signal phase \\  & Information & and upcoming signal phases of the traffic \\  & & light \\ \hline \multirow{4}{*}{(4.2) Traffic light prioritisation} & \multirow{2}{*}{Designated vehicles (e.g., tram or public} \\  & & bus) are given priority at intersections \\ \cline{1-1} \cline{3-3}  & & \\ \end{tabular}
\end{table}
Table 2: First candidates of services and use cases for the roll-out.  
network. Specifically, the study examined their impact on traffic safety, efficiency, and environmental performance, as well as the level of driver acceptance.
# Optical Characterization of Homogeneous and Heterogeneous Intralipid-Based Samples
## 4 Results and Discussion
### TRT Results
#### 4.3.2 Absorptive Inclusion Results  
According to Section 3.2.2, for this series of measurements, the source beam and the detector were positioned upon the opposite surfaces of the phantom in a coaxial configuration. Each inclusion moved along the x-direction inside the host medium transversally to the source-detector axis. The time-resolved transmittance was measured for each scanning position and the Gaussian perturbation model, which is described in Section 2.2.1 through Equations (15)-(18), was used for retrieving the value of the absorption coefficient of the inclusion \(\mu_{a,inc}\) through a fitting procedure with the experimental measurements. The quantity \(\mu_{a,inc}\) was the only parameter of the fitting because the absorption and the reduced scattering coefficients of the host medium were previously retrieved by fitting the model Equation (14) with the time-resolved transmittance of the phantom without inclusion. Black points of Figure 8 shows the retrieved values of the absorption coefficient \(\mu_{a,inc}\) as function of the scanning position, for all the considered inclusions. As can be seen, a maximum at the location of the center of the inclusion was observed and it was identified as the absorption coefficient value of the inclusion. Experimental points were very well fitted with a Gaussian curve (red line) that allows for a better estimate of the center of the inclusion, its size R, and its absorption coefficient according to Equation (15). The absorption coefficient of the inclusions was retrieved with an error that increased as its value increased. Overall, we obtained an accuracy better than 14% when the relative perturbation parameter \(\Delta\mu_{a}/\mu_{a}\) was less than 140%.  
Figure 7: **(a)** Reduced scattering coefficient of Intralipid suspensions (homogeneous slab) with different Intralipid concentration (C), experimentally obtained by time-resolved transmittance (TRT), as a function of C. Red line: fitted linear curve with intercept = \(-0.2\pm 0.2\) mm\({}^{-1}\) and slope = \(0.165\%\pm 0.009\%\)/mm\({}^{-1}\); linear correlation coefficient r = 0.98; absorption coefficient = \(0.0035\pm 0.0008\) mm\({}^{-1}\). **(b)** Absorption coefficient of Intralipid suspensions (homogeneous slab) with different ink concentration (C\({}_{\rm I}\)), experimentally obtained by TRT, as a function of C\({}_{\rm I}\). Red line: fitted linear curve with intercept = \(0.00216\pm 0.00001\) mm\({}^{-1}\) and slope = \(0.02804\%\pm 0.00001\%\)/mm\({}^{-1}\); linear correlation coefficient r = 1; reduced scattering coefficient = \(0.250\pm 0.014\) mm\({}^{-1}\).