esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Research on the Limits of Longitudinal Slope Design Criteria
#### 3.3.2 Critical Length  
On uphill sections, the speed of heavy vehicles is inevitably affected, especially on steep and long inclines, where vehicles experience continuous deceleration. This induces a strong psychological reaction in drivers and rapidly increases the speed differential with other vehicles, posing potential risks to road safety. To ensure transportation safety and efficiency, the \"Specification\" adopts a 20 km/h speed loss of vehicles as a criterion for calculating the critical length. However, the \"Specification\" does not account for the fact that the same speed loss of vehicles may elicit different perceptions among drivers at different design speeds. Therefore, \(DV\) is introduced as a measure that not only controls the speed loss of vehicles during climbing but also reflects the perceptual differences among drivers at different design speeds. A smaller \(DV\) value indicates a smoother speed variation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Design Speed (km/h)**} \\ \cline{2-4}  & **120** & **100** & **80** \\ \hline Maximum grade (\%) & 2.5 & 3.5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum grades for the power performance of the test vehicle.  
Figure 14: Relationship between initial climbing speed, power-to-weight ratio, and \(DV\).  
during vehicle climbing, thus enhancing road safety. Based on the literature references, the criteria for defining the climbing speed decay degree of vehicles are presented in Table 7.  
The \"Specification\" does not impose restrictions on the length of longitudinal slopes with gradients below 3%. However, considering that prolonged driving on the same linear road may lead to driver fatigue, it is advisable to comply with the limitations on the length of road linear units as per the linear design. For longitudinal slopes not less than 3%, this study uses a \(DV\) value of 0.15 as the limiting criterion to determine the critical length corresponding to different slope gradients under road conditions with design speeds of 120 km/h, 100 km/h, and 80 km/h, as shown in Table 8. On a road with a longitudinal slope gradient of 3.0% and a design speed of 80 km/h, the vehicle speed loss value approaches the equilibrium speed corresponding to the slope and enables stable driving, thus obviating the need to impose restrictions on the length of the longitudinal slope.
## 1 Introduction
### Freehand 3D Ultrasound Imaging  
Freehand 3D ultrasound imaging adopts conventional ultrasound technology to build up 3D volumes or surfaces from a number of 2D B-scans acquired in succession (see Figure 1). It consists of tracking a standard 2D ultrasound probe using a 3D spatial localizer (magnetic, mechanical, or optical). The localizer is attached to the probe, and can continuously measure the 3D position and orientation of the probe while the physician moves the probe slowly and steadily over a particular anatomical region of the patient. The measured outputs of the 3D position and orientation are used for the localization of B-scans in the coordinate system of the spatial localizer. In order to establish the transformation between the B-scan coordinates and the 3D position and orientation of the probe, a calibration procedure is necessary [10; 11; 12].  
In order to obtain a 3D volume or surface model of the VOI (e.g., the lesion to be ablated), the 2D B-scans of the VOI should be acquired first, and a 3D reconstruction method should be applied to these B-scans.  
Figure 1: Freehand 3D US system. This is also the setup for the experiments. The spatial localizer is an electromagnetic tracking device called AUORA from Northern Digital Inc. (Waterloo, ON, Canada). IGS (image-guided surgery) software serves as the 3D surface reconstruction and visualization program.  
Freehand 3D ultrasound has a noteworthy characteristic: the recorded B-scans of freehand 3D ultrasound are non-parallel and may intersect mutually, because the movement of the ultrasound probe is free and unrestricted in space. These non-parallel B-scans are different from the parallel slices of CT or MRI imaging. When the B-scans are segmented into contours, the contours are also non-parallel, which increases the difficulty of 3D reconstruction for freehand 3D ultrasound.  
Presently, the 3D reconstruction methods for freehand 3D ultrasound can be divided into two categories: volume reconstruction and surface reconstruction.
# Advances in Riblets Design
## 3 Experimental Investigation of Riblets Performance
### Flight Tests and In-Service Applications of Riblets
[66] conducted flight tests to investigate the use of micro-riblet coatings on the fuselage and wing of an Airbus 300-600 ST BELUGA. The method of fabrication is also discussed in the next section. The research included a special method for data gathering, continuously carried out for 12 months, in which the evaluation of riblet effectiveness by replication and testing in the laboratory aimed to evaluate the degradation effects. In the meantime, a collaboration between Lufthansa, Fraunhofer IFAM and BWM GmbH investigated the effectiveness of riblet patches on the wing of an Airbus 340-300 [85]. The main target of this activity was the time of implementation of riblets over the aircraft surface, and no results were found about the effectiveness of their method. \begin{table}
\begin{tabular}{l l l l} \hline
**Institute/Airliner/Aircraft** & **Year** & **Description** & **Riblet** & **Drag Reduction** \\ \hline \multirow{3}{*}{Boeing T-33 trainer [103]} & \multirow{3}{*}{1987} & Wing sweep = 15\({}^{\circ}\) & \multirow{3}{*}{3M} & Friction drag reduction: \\  & & \(Re=1.45\sim 4.43\times 10^{6}\), & & 6–7\% \\  & & M = 0.35–0.7 & & \\ \hline \multirow{6}{*}{Learjet 28/29/NASA [76]} & \multirow{3}{*}{1988} & Lightweight plastic film was & \multirow{3}{*}{3M} & \multirow{3}{*}{Friction drag reduction: \\  & & applied by use of two rakes and & & \\  & & two drag balances & & 6\% \\ \cline{1-1} \cline{3-3}  & & \(\text{h}=\text{s}=0.03\) mm (first case) & & \\ \cline{1-1} \cline{3-3}  & & \(\text{h}=\text{s}=0.075\) mm (second case) & & \\ \cline{1-1} \cline{3-3}  & & \(\text{Re}=1\)–2.75 \(\times\) 10\({}^{6}\), M = 0.3–0.7 & & \\ \hline \multirow{3}{*}{Airbus 300–600 [105]} & \multirow{3}{*}{1988} & Riblet films with triangular cross & \multirow{3}{*}{3M} & Only the deterioration and \\ \cline{1-1} \cline{3-3}  & & \(\text{sections}\), patched in 15 different & & \\ \cline{1-1} \cline{3-3}  & & \(\text{areas Metallic riblet foil}\) & & \\ \hline \multirow{3}{*}{Airbus 320 [105]} & \multirow{3}{*}{1989} & Riblet films with triangular & \multirow{3}{*}{3M} & Net drag reduction: \\ \cline{1-1} \cline{3-3}  & & \(\text{70\% covering area}\) & & 2\% \\ \cline{1-1} \cline{3-3}  & & \(\text{(600\) square meters)} & & \\ \hline \multirow{3}{*}{Airbus 340/Airbus/Cathay} & \multirow{3}{*}{1996} & Triangular riblets with s = 0.06 mm & \multirow{3}{*}{3M} & Friction drag reduction: \\ \cline{1-1} \cline{3-3}  & & \(\text{30\% covering area}\) & & 4\% \\ \cline{1-1} \cline{3-3} \cline{1-1} \cline{3-
## 3 Results and Discussion
### Fesem  
Figure 5 illustrates the surface morphology of extracted celluloses and CMSC. The extracted cellulose samples clearly show unique cellulose structure. The presence of a smooth surface indicates the possibility of hemicellulose and lignin present in the amorphous state (Figure 5B-E). In Figure 5A, it is observed that the sample exhibits the compact structure of fibrils, and the surface roughness is higher due to the crystallinity and rigidity. The appearance in CMSC sample exhibits a very different surface morphology where it is more granular than cellulose. It clearly indicates smooth particles, that are clumped up together due moisture trapped within the particles.  
Figure 4: X-ray diffractograms of cellulose A (preparation 1) (**A**); cellulose B (preparation 2) (**B**); cellulose C (preparation 3) (**C**); cellulose D (preparation 4) (**D**); cellulose E (preparation 5) (**E**); CMSC (**F**).  
Figure 5: FE-SEM images of cellulose A (preparation 1) (**A**); cellulose B (preparation 2) (**B**); cellulose C (preparation 3) (**C**); cellulose D (preparation 4) (**D**); cellulose E (preparation 5) (**E**); and carboxymethyl sago cellulose (CMSC) (**F**). All cellulose preparation was at \(\times\)800 magnification and CMSC was at 2200\(\times\) magnification.
## 2 Materials and Methods  
### Research Area  
The range of the study area is 73\({}^{\circ}\)40\({}^{\prime}\) E-135\({}^{\circ}\)2\({}^{\prime}\) E and 3\({}^{\circ}\)51\({}^{\prime}\) N-53\({}^{\circ}\)31\({}^{\prime}\) N. Located in Eastern Asia and near the Pacific Ocean (Figure 1a), it covers a land area of about 960 \(\times\) 10\({}^{4}\) km\({}^{2}\) and an ocean area of about 473 \(\times\) 10\({}^{4}\) km\({}^{2}\). The topography presents a terraced distribution, decreasing from West to East. It is mainly dominated by plateaus and mountains, accounting for 59.3% of the land. The Tibetan Plateau is located in Western China, whose average altitude is higher than 4000 m. The direction of the mountains is mainly East-West and Northeast--Southwest. It is divided into three steps by high mountains. The divided lines are the Kunlun-Qilian-Hengduan Mountains and the Daxinganling-Taihang-Wu-Xuefeng Mountains. The plains are situated on the third terrace, encompassing regions such as Northeast China, North China, and the Middle and Lower Yangtze Plain. The distinct geographical positioning and elevation gradients result in regional climatic variations, categorized as continental, monsoon, and plateau mountain climates, with the monsoon climate being the most prominent. Temperature exhibits a decreasing trend from lower to higher latitudes, with the Tibetan Plateau acting as a low-value center due to its uplift. Precipitation patterns are influenced by the region's land--sea location and topography, showing a decline from Southeast to Northwest. Land use and land cover predominantly comprise grassland and unutilized land, accounting for 46.31% and 23.72% in 2020, respectively (Figure 1c). Considering factors like topography and climate, the research area was divided into eight regions based on provincial administrative districts (Figure 1b), including Northwest China (NWC), North China (NC), Northeast China (NEC), East China (EC), Central China (CC), South China (SC), Southwest China (SWC), and Qinghai-Tibet (QT).  
Figure 1: Spatial distribution of (**a**) location, (**b**) zone, (**c**) land use and land cover in China.
# Risk Analysis of a Two-Level Supply Chain Subject to Misplaced Inventory
## 4 Decision Policies of the Supply Chain with RFID Implementation
### The Benefits of RFID Implementation  
We derive the benefits of RFID implementation by comparing them with the case when information is shared among supply chain participants. We specify the cost thresholds at which RFID adoption is more profitable for supply chain participants. Let's define \(t_{\varepsilon}^{R}\) (\(t_{\varepsilon}^{S}\)) as the cost threshold for the retailer (supplier). It is only profitable for the supplier (the retailer) to adopt RFID when the actual RFID costs \(t\) is no greater than \(t_{\varepsilon}^{S}\) (\(t_{\varepsilon}^{R}\)).  
**Proposition 2**.: _The retailer has stronger incentives for RFID adoption compared with the supplier, i.e., \(t_{\varepsilon}^{R}\geq t_{\varepsilon}^{S}\)._  
**Proof.** Please refer to the Appendix A for the proof.  
The retailer benefits more from RFID technology even though he is the follower in the supply chain. In the situation when \(t_{\varepsilon}\leq t_{\varepsilon}^{S}\), RFID adoption is a win-win strategy for both parties. When \(t_{\varepsilon}^{S}<t_{\varepsilon}<t_{\varepsilon}^{R}\), the retailer can benefit from RFID deployment; however, from the supplier's perspective, it is not cost-effective. In other words, the cost savings from RFID implementation are not symmetrically distributed among supply chain participants.
## 4 Experimental Evaluation
### Primary-Secondary nodes radio link experiment  
One secondary node and one primary node are placed in an obstacle-free environment from 0 m to 120 m in 10 m intervals as shown in Figure 7. At each distance the secondary node generator is swung 30 times and therefore 30 frames are transmitted. Each time the primary node receives a frame it is stored in the EEPROM memory for its download in a PC computer. We have repeated this test for 12 different positions and 6 different nodes.  
Figure 8 shows the error rate average of the communication system. We observe that for short distances (less than 20 meters) all the frames transmitted arrive correctly at the primary node. When the emitter moves away from the receiver, some frames are not received and get lost. Only 30% of the frames arrive at the receiver at 100 m and no frame is received at larger distances. Moreover, we observe after 30 m some of the frames received are not correctly decoded, with a maximum error of 40% when the nodes are at their maximum transmission range.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 2 Primary Classifications of Electrical Energy Losses
### Technical Energy Losses
#### 2.1.2 Leakage Losses  
\[\Delta P_{2}=U^{2}\times G, \tag{5}\]  
where,  
* U--voltage between cable core and insulation (V).
* G--dielectric leakage conductance (\(1/\Omega\)).
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 3 Cases of Study
### Case B: Subvertical Rock Cliff of Quartzites--Ornea (CN, Italy), Ligurian Alps
After the traditional KA, the information of the discontinuity mapped onto the 3D model and the point cloud representing the outcrop were formatted as required by the ROKA algorithm. Due to its big dimension and resolution (98 million points and 1000 pts/m\({}^{2}\)), the point cloud was subsampled to 574,708 points (\(\approx\)20 cm point spacing; \(\approx\)25 pts/m\({}^{2}\)). Considering the visible dimension of the discontinuities represented by a Baecher's disc, 4667 \"real\" intersections (as described in Section 2.3) were identified. Subsequently, the ROKA algorithm was performed using a scan-radius of 1 m (therefore with a scan-sphere and a scan-plane of around 4.19 m\({}^{3}\) and 3.14 m\({}^{2}\), respectively). The different orientations of the slope calculated using the specified scan-radius are shown in Figure 17.  
In Figure 18, the frequency of the discontinuities and discontinuities intersections are highlighted. The results of the ROKA algorithm KA for the intersected discontinuities and discontinuities intersections are indicated in Figures 19 and 20. Figure 19: Rendering of the 3D model indicating the results of the ROKA Kinematic Analysis of the possible modes of failure. The color scale represents the critical value of the discontinuity planes that could activate (**a**) planar failure and (**b**) flexural toppling (see Section 2.5 for the critical value definition). Figure 18: Rendering of the 3D model of the slope indicating (**a**) the number of the intersections between the scan-plane and discontinuities for every point of the cloud where the scan-plane (representing the local geometry of the slope) is centered and (**b**) the number of the intersections between the scan-plane and the discontinuity intersections. The statistics of the results obtained by the ROKA algorithm are reported in Tables 8 and 9.
## 4 Methods
### Data Analysis  
A total of 560 notifications on the occurrence of defects during the warranty were identified. The research concerned 353 defects qualified as valid, which accounted for 63.04% of all reports. Additionally, 207 defects were considered unfounded, which accounted for 36.96% of the reports. Table 2 presents the summary statistics of the reported defects. Buildings A and B are larger both in terms of the number of floors and the number of flats. Moreover, they were accepted earlier than Building C, which significantly affected the number of reports identified in the study.  
What draws attention to the data presented is the similarity in the percentage of valid defect reports in the individual buildings. The comparability of the results obtained is the result of the relatively large size of the research sample, which eliminates the divergence of results.  
Taking into account the different warranty periods on individual buildings, in order to unify the presentation of statistical characteristics of the testing ground, the occurrence of both the reports of defects as well as their justification was calculated per one month of warranty per one commercial unit. The results of the calculations are presented in Table 3.  
The authors also analyzed the validity of the defects reported within 4 months from the date of acceptance, which is related to the typical time of finishing the premises by the owners (Table 4), and after 12 months, namely, the period when a significant number of premises are already finished and used (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Number of Defects**} & \multicolumn{2}{c}{**Valid**} & \multicolumn{2}{c}{**Unfounded**} \\ \cline{3-5}  & & **No.** & **\%** & **No.** & **\%** \\ \hline A & 61 & 34 & 55.74 & 27 & 44.26 \\ B & 43 & 24 & 55.81 & 19 & 44.19 \\ C & 12 & 9 & 75.00 & 3 & 25.00 \\ Total & 116 & 67 & 57.76 & 49 & 42.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Defect statistics after 3 months from acceptance.
The analysis performed helped us to formulate two important statements. One is that the owners are very active in making warranty claims in the first 3 months from the date of acceptance. The analysis revealed that 116 reports took place in the first months of use, which accounts for 20.71% of all reports included in the study within only \(11\%\) of the study's timeframe. The other observation is the increasing percentage of reported defects, which for the first 3 months was 57.76%, for 12 months 60.48%, and at the end of the study (after half the warranty period) 63.04%. The validity of the defects is presented cumulatively in Figure 2. The deviation of the result for Building C for the first three months of the warranty was due to its smaller size in relation to the other two, which was also associated with a small number of reports, namely, 12 of them.
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 2 Related Work
### Salient Object Segmentation in Images  
Salient object segmentation in images is a research area that has been greatly developed in the past twenty years, in particular since 2007 [40].  
Early approaches treated saliency object segmentation as an unsupervised problem and focused on low-level and mid-level cues, such as contrast [32; 41], focussess [33], spatial properties [42; 43], spectral information [44], objectness [35], etc. Most of the cues build upon foreground priors. For example, the widely used contrast prior believes that the salient regions present high contrast over the background in certain contexts [42; 45], and the focusness prior considers that a salient object is often photographed in focus to attract more attention. From the opposite perspective, the background prior was first proposed by [PERSON] et al. [43], who assumed the image boundaries are mostly background and built a saliency detection model based two background priors, i.e., boundary and connectivity. After that, some approaches [34; 46; 47; 48] successively appeared. Unfortunately, these methods usually require a prior hypothesis about salient objects, and their performance heavily depend on the prior's reliability. Besides, the methods that only use purely low-level/mid-level cues face difficulties in detecting salient objects in complex scenes due to their unawareness of image content.  
Recently, learning-based methods, especially deep networks methods (i.e., CNN-based models and FCN-based models), have attracted much attention because of their ability to extract high-level semantic information [4; 13; 49]. In [13], two neural networks, DNN-L and DNN-G, were proposed to respectively extract local features and conduct a global search for generating the final saliency map. In [5], [PERSON] and [PERSON] introduced a neural network with fully connected layers to regress the saliency degree of each superpixel by extracting multiscale CNN features. While these CNN-based models with fully connected layers that operate at the patch level may result in blurry saliency maps, especially near the boundary of salient objects, in [50], fully convolutional networks considering pixel-level operations were applied for salient object segmentation. After that, various FCN-based salient object segmentation approaches were explored [51; 52; 53] and obtained impressive performance.  
However, most of these methods focus on independent foreground or background features, and only several models [54; 55] pay attention to both of them. To the best of our knowledge, few models explicitly model the constraint relationship between them, although it may be very helpful in complex scenes. Therefore, in this work, we simultaneously consider foreground and background cues as well as their complementary relationships and optimize their joint objective by using the powerful learning ability of deep networks.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 2 Multi-Layer Air Transport Networks
### Mapping of Air Transport Networks
#### 2.2.1 Coupling Relationship between \(G_{an}\) and \(G_{ARN}\)  
For the city-pair airline network \(G_{AN}\), each city-pair airline \(l_{i,j}\in L(G_{AN})\) from city \(a_{i}\) to \(a_{j}\) must have at least one corresponding routing path in the air route network \(G_{ARN}\), and one routing path is a sequence of air route segments. For example, as shown in Figure 1, for a city-pair airline \"Beijing \(\rightarrow\) Nanjing\", the corresponding routing path on the air route network \(G_{ARN}\) includes five air route segments, that is, \"Beijing \(\rightarrow\) IDKUP\(\rightarrow\) Jinan\(\rightarrow\) DALIM\(\rightarrow\) UDINO\(\rightarrow\) Nanjing\".  
For each air route segment \(r_{i,j}=(w_{i},w_{j})\in R(G_{ARN})\) in the air route network \(G_{ARN}\), we can obtain all the city-pair airlines, whose routing path contains the air route segment \((w_{i},w_{j})\). For example, for the air route segment \"DALIM\(\rightarrow\) UDINO\", we can obtain the city-pair airlines, whose routing path contains the air route segment \"DALIM\(\rightarrow\) UDINO\", such as a city-pair airline \"Beijing \(\rightarrow\) Nanjing\".