esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data
### Hierarchical Bayesian Modeling
#### 3.1.3 Gaussian Predictive Processes (GPP) Model  
The GPP model is mainly used to predict values for large spatial datasets that are sparse, and it applies sparse matrix algorithms to overcome the high computational cost when modeling the parameters of the large dataset, unlike the GP and AR models that use dense matrix algorithms.  
The GPP models is a modified version of the AR model, where the spatiotemporal random effects error \(\eta_{(s_{ij};t_{0})}\) is distributed as stationary parameter with zero mean in the AR model, while in the GPP model, we define the random effects \(\eta_{(s_{ij};t_{0})}\) at each spatial point \((s_{i},s_{j})\), \(i,j=0,\ldots,\ m\), which we call knots. Let \(\omega_{It}\) denote the spatial random effects at these locations \(\omega_{It1}=(\omega_{I_{(s_{ij};t)}},\ldots,\omega_{I_{(s_{m};t)}})\). We define knots of spatial points as a grid before fitting the model. Defining the knots grid reduces the computational complexity of the GPP model when modeling large datasets. The GPP defines the random effects of each spatial point inside the boundary of the selected grid. Different grid sizes can be selected, such as \(4\times 4\), \(6\times 6\), \(8\times 8\), \(10\times 10\), \(12\times 12\), and \(16\times 16\). The knot length needs to be equal to or less than the number of spatial points. We define the hierarchy of the GPP model in Equations (23) and (24):  
\[Y_{I(s_{ij};t)}=f(x_{I})_{(s_{ij};t)}+\epsilon_{I(s_{ij};t)} \tag{23}\]  
\[f(x_{I})_{(s_{ij};t)}=\rho\omega_{It-1}+\chi_{I(s;t)}\beta+\eta_{(s_{ij};t)} \tag{24}\]  
Let \(\theta=(\rho;\beta;\sigma_{\omega^{\prime}}^{2};\dot{\varphi};\dot{\
u})\) denote all the parameters of the GPP model, and we integrate the posterior predictive distribution for \(Y_{I(s_{ij};t)}\) over the model parameters with respect to the joint posterior distribution as follows:  
\[\pi(Y(s,t^{\prime})|y)=\int\pi(Y_{I}(s,t^{\prime})|N_{I}(s,t^{\prime}),\sigma_ {\varepsilon}^{2})\times\pi(N_{I}(s,t^{\prime})|\theta,N,y^{\ast})\times\pi( \theta,N,y^{\ast}|y)dN_{l}(s,t^{\prime})dNd\theta dy^{\ast} \tag{25}\]
###### Abstract  
The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in \"smart\" tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.  
Keywords:neural networks; LSTM; embeddings; trajectories; motion behavior; smart tourism +
Footnote †: journal: Applied
## 5 Conclusions  
The paper proposes three transmission/reception timing schemes that maximize the time axis use efficiency in one-dimensional linear sensor network environments to improve the resource efficiency for high rate transmission. Schemes 1 and 2 are those in which all nodes are transmitting or receiving data at all times with no idle time. Therefore, these schemes have the highest time use efficiency. Scheme 3 has the time use efficiency, which is 2/3 of the maximum time use efficiency but obtains more SIR gains. SINRs by node for the three schemes through mathematical analyses and power levels by node are derived based on the transmission rate conditions. In the numerical results, the power distributions by node, rate bounds, power levels depending on the rates and number of nodes, and network split gains are examined in a range of environments. Scheme 2 is found to be the best of the proposed schemes and can guarantee approximately 50% higher transmission rates at the same power in the numerical results. In the simulation results, Scheme 2 is compared with the existing packet train method. Scheme 2 outperforms the packet train method in transmission efficiency or power consumption but it has a limitation in network deployment geometry.  
Figure 16: Power levels according to the node by the simulation and analysis in Scheme 2.
## 3 Pathogenesis of AD
### The Amyloid Hypothesis
#### 3.1.2 Amyloidogenic Pathway  
The amyloidogenic process involves the proteolytic cleavage of APP by \(\beta\)-secretase (BACE1) and the \(\gamma\)-secretase complex in a sequential manner [63; 67]. In this regard, BACE1 is a membrane-spanning aspartyl protease [63], whereas \(\gamma\)-secretase is an intramembrane aspartyl protease composed of four proteins: PEN2, nicastrin, presenilin, and anterior pharynx defective 1 (APH-1) complexed together [68; 69]. \(\gamma\)-Secretase converts the complex into insoluble and neurotoxic A\(\beta\) fragments [70]. The first and rate-limiting step in this process is the \(\beta\)-secretase cleavage [66; 71]. \(\beta\)-Cleave releases the sAPP ectodomain, and a 99 amino acid APP carboxy-terminal fragment (C99) can be cleaved further at numerous sites by \(\gamma\)-secretase. APP cleavage by \(\gamma\)-secretase can result in amyloid peptides with varying chain lengths, such as A\(\beta\)37; 38; 39; 40; 42, and 43 [72]. These peptides form A\(\beta\) oligomers, whereas aggregated plaques are produced by further polymerization [63]. In this respect, A\(\beta\)42 and A\(\beta\)40 are the two primary A\(\beta\) species found in the brain. Although soluble, A\(\beta\)40 is substantially more prevalent than soluble A\(\beta\)42; A\(\beta\)42 has a greater tendency for aggregation because of the hydrophobicity within its two terminal residues. In addition, A\(\beta\)42 is the key element of amyloid plaques, which has been found to be neurotoxic [66; 73; 74]. As a result, A\(\beta\)42 is assumed to be a critical participant in plaque development and AD pathogenesis [75]. Furthermore, it has been demonstrated that the levels of A\(\beta\)38, A\(\beta\)42, and the A\(\beta\)42/A\(\beta\)38 ratio in cerebral spinal fluid may be utilized to differentiate AD from other dementias [3; 76]. On the other hand, fibrillar A\(\beta\) has been demonstrated to enhance cytokine and nitric oxide generation in CD14, TLR2, and TLR4-dependent microglia [77; 78]. [PERSON] et al. discovered yet another signaling mechanism via which A\(\beta\) may cause inflammation [79]. These authors discovered that A\(\beta\) causes inflammatory signaling by forming heterodimers of Toll-like receptors 4 and 6. Interaction of A\(\beta\) with the scavenger receptor CD36 regulates the assembly of this heterodimer. In this context, participation of CD14, CD36, and Toll-like receptors in A\(\beta\)-induced microglia activation clearly demonstrates that innate immunity is associated with AD pathogenesis [80; 81].
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 3 Front-End
### Object Detection  
At present, object detection based on deep learning has become a mainstream method. The state-of-the-art algorithms, such as YOLO [38], Faster R-CNN [39], and Mask R-CNN [40], are favored by researchers. Using the YOLO and Faster R-CNN, we can obtain the semantic labels, probability values, and bounding rectangles (BRs) of objects. However, the BR is only an approximate representation of the object area--it may include some pixels that do not belong to the object. To solve this problem, Mask R-CNN adds a masked prediction branch on the basis of Faster R-CNN object detection. It can further determine whether the pixels belong to the object and provides more accurate results. We thus use Mask R-CNN to obtain object semantics. Figure 4 shows example results of this algorithm.  
Figure 4: Detected objects by Mask R-CNN: (**a**) a bed and many books; (**b**) a chair, an oven, a microwave, and a cup; and (**c**) two toilets, two sinks, a chair, and a TV.  
Figure 3: Comparison of split and joint interpolation trajectories.  
The algorithm can detect thousands of objects, so the types need to be limited. We detected six object categories, including bed, couch, sink, toilet, microwave, and oven. These objects are usually not moved frequently and can be used to determine room concepts. For example, a room containing beds is most likely a bedroom, and couches have a large probability of being located in the living room. Considering small objects, such as cups, bowls, and forks, are frequently moved, they are not detected. When the navigation goal points are related to these objects, goal reasoning will be performed according to the instances of the six object categories and room concepts.
## 3 Conditional Compression
### Compression in the presence of shared data  
[PERSON], [PERSON] and [PERSON] [5] and [PERSON] and [PERSON] [6] exploit the performance of dictionary based compressors in the case of pre-existing shared data by including in the dictionary the pre-existing strings. They consider real life situations in which a Sender and a Receiver have to communicate and they share knowledge of data, as it is common for example in a client-server environment where the server manages pervasive devices, or when a product distributor has to ship a new release of a software package to clients that hold an older release. Their idea is to extend the dictionary used by the dictionary based compression algorithm by using the shared data. This will produce longer matches during the compression process and will avoid starting the compression process with an almost empty dictionary.  
The dictionary words are sent from the encoder to the decoder by triples, called segment specifiers. The triples have the form (file-id; offset: length) and indicate clearly to the decoder the id of the shared file from which the dictionary word has to be retrieved. This scheme is appropriate when the Sender has strong computational resources and no time constraints in compressing the data: the Sender has to find an effective way to use the dictionary (that is distributed also into the shared files) to compress the file. The Receiver is instead fast and simple: it just copies and pastes strings by using the Sender's instructions.  
They experimentally show that there is a gain in compression (ranging from a 1-2% and in some cases up to 6-7% in the size of the compressed file) that is due to the fact that the compressed file does not contain the whole dictionary but rather parts of this dictionary are already available at no cost: they are known as shared data.
## 3 Results and Discussion
### Sugars Content  
The sugars content in untreated and PEF-treated freeze-dried pumpkin is presented in Figure 4. In general, regardless of the freeze-drying temperature, the untreated material contained significantly more sucrose. Furthermore, the amount of glucose and fructose in the PEF-treated pumpkin was in most cases significantly higher. For example, the sucrose content of intact freeze-dried pumpkin was equal to 6.32 and 4.60 g/100 g d.m., for 40 and 60 \({}^{\circ}\)C, respectively. In turn, the PEF-treated material exhibited 3.89-5.55 g/100 g d.m. when the shelf temperature was set to 40 \({}^{\circ}\)C, and 2.99-4.28 g/100 g d.m. when it was set to 60 \({}^{\circ}\)C. At the same time, the levels of glucose and fructose content for PEF-treated samples were higher by 11.0-44.2% and 0.3-30.2%, respectively.  
Figure 3: Content of total phenolic compounds, \(TPC\) (**a**), and total carotenoid, \(TCC\) (**b**), in untreated and PEF-treated pumpkin freeze-dried at 40 \({}^{\circ}\)C (**a**) and 60 \({}^{\circ}\)C (**b**) depending on the specific energy input \(W_{p}\). -e Different letters indicate significant statistical differences (Tukey’s HSD, \(p<0.05\), \(n=3\)).  
Several studies explained this phenomenon by sucrose inversion. The \(\alpha\)-1.2-glycosidic bond, which combines glucose and fructose, can be broken in several ways, for instance, by applying a high temperature, chemicals, or enzymes. As a result of the hydrolysis process, sucrose can be broken into fructose and glucose monosaccharides [41]. PEF application can apparently have an influence on the inversion process of sucrose. In this case, enzymatic hydrolysis at low pH takes place. Due to the electroporation of the cells, PEF facilitates the internal diffusion of sugars and other substances, e.g., enzymes. Considering that enzymes can reach the substrate more easily, in this case sucrose, enzymatic hydrolysis happens faster. The existing literature also demonstrates that PEF treatment may increase the activity of native and even denaturized sucrose invertases [42]. Moreover, the kinetics of most chemical and biological processes are faster at higher temperatures. Also, the activity of sucrose invertases was reported to depend positively on temperature [43]. That could explain the lower sucrose content in the samples dried at 60 \({}^{\circ}\)C. It is worth emphasizing that an enhancement of sucrose inversion was also found because of ultrasound utilization [44].  
Two-way ANOVA proved that energy input, temperature, and the interaction between those parameters alike had significant (\(p<0.05\)) effects on sucrose and glucose content, whereas the fructose amount was influenced only by temperature and energy input. What is interesting, in the case of fructose content, is that the value of \(\eta^{2}\) found for the effect of energy was much lower (0.774) than for temperature (0.909), while for sucrose it oscillated between 0.961 and 0.982, and for glucose between 0.861 and 0.882.
## 3 Results and Discussion
### Adsorption Isotherm
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Adsorbents** & **Adsorption Capacity** & **References** \\  & **(mg g\({}^{-1}\))** & **References** \\ \hline Ag-Fe\({}_{3}\)O\({}_{4}\)–polydopamine & 45 & [57] \\ Magnetic carboxyl functional & 58 & [58] \\ nanoporous polymer & 476 & [19] \\ NaA & 65 & [21] \\ Zeolite-A/reduced graphene oxide & 667 & [22] \\ Magnetic zeolite & 10 & [23] \\ Activated carbon & 114 & [59] \\ Citrus hystrix-rGO & 276 & [60] \\ Eco-friendly rGO & 122 & [5] \\ ASAC & 37 & [14] \\ \(\beta\)-cyclodextrintrin/MGO & 94 & [51] \\ ZGO10 & 119 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum adsorption capacities of ZGO10 in relation to other adsorbents. Figure 10: Illustrative diagram of the adsorption mechanisms of MB\({}^{+}\) onto ZGO including arrows identifying distinct material regions (surface and pores).
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 1 Introduction
[19] proposed a procedure using a mass-conserving diagnostic model that incorporates a detailed map of local topography to improve predictions of wind direction. The authors of [20; 21] established coupled dynamic finite element models of a transmission tower line system and the equivalent acceleration of fluctuating wind was simulated. However, studies on wind speed diversity are lacking. [PERSON] et al. [22] analyzed the cascading failures of towers due to extreme straight-line winds and obtained fragility curves that describe the probability of structural failure for both intact and failed adjacent towers. In this study, we focused on the typical canyon terrain and complex and variable wind fields to obtain the vibration law of transmission wires under the influence of certain conditions. The purpose of this study is to prevent the wind deviation tripping of wires caused by too close wire spacing. This law can be used to carry out the safe construction and layout of transmission wires. Furthermore, this law can be combined with wind-monitoring devices to form a safety early warning system for transmission wires.
# Equatorial Plasma Bubbles: A Review
## 2 Observations of EPBs and Associated Irregularities
### Radar Observations
Figure 2: RTI maps obtained from MST radar observations at Gadanki on two consecutive nights: 6 April 2011 (**top panel**) and 7 April 2011 (**bottom panel**) show that EPBs that developed on the bottom side of the equatorial F region after sunset, starting with different initial perturbations on these two days, also evolved differently with time and gave rise to plume structures with entirely different characteristics. The plume structures that appear in the RTI map are images of a succession of EPB structures that drift into the radar’s field of view at different times.
## 3 Measurement Conditions
### Rgb Measurements  
RGB data was captured by the camera with basic parameters set as 1920 \(\times\) 1080 recording resolution, 30 frames per second. The RGB camera was placed alongside IR camera. The purpose of data capturing from the RGB measurements focused on tracking the change of belt edge position that happened during the experiment. As stated before, only one situation has been taken into consideration: position change during belt mistracking to the left side of the cylinder. An exemplary frame during that process can be seen in Figure 11. Thanks to the clear difference between belt and cylinder colors, simple Canny edge detection was applied without any previous or further preprocessing necessary. Effects of edge detection can be seen in Figure 11.  
The position change of the belt has been measured on the central part of the cylinder relative to the camera position. It can be described as the most to the side (either left or right) visible part of the belt. The corresponding side of the cylinder is taken as a point of reference that allows tracking of the difference from the starting or desired belt position. Considering that the maximum dimensions of both belt and cylinder are known, a measurement of a single belt edge movement is sufficient for whole belt position tracking and determining if belt mistracking has occurred. As in the case of IR measurements, this operation has also been repeated for each frame of the recorded data.  
Figure 11: Recorded RGB frames before ad after processing: (**a**) exemplary RGB frame, (**b**) exemplary RGB frame after edge detection.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 2 Modeling Preparation
### Problem Analysis  
Assuming a four-way unsignalized intersection, within the optimization time window, there are only two vehicles in each direction, i.e., \(|\mathcal{N}_{i}|=2,i\in\mathcal{I}=\{1,2,3,4\}\). According to permutation and combination theory, the total number of sequential combinations for these vehicles to pass through the intersection's conflict zone is \(A_{8}^{8}\). However, considering that vehicles from the same direction can only pass through the conflict zone in their order of arrival, the actual feasible passing sequences are \(A_{8}^{8}/\left(A_{2}^{2}A_{2}^{2}A_{2}^{2}A_{2}^{2}\right)\). In summary, when the number of vehicles in each direction is \(|\mathcal{N}_{i}|,i\in\mathcal{I}=\{1,2,3,4\}\), the total number of feasible passing sequences is \(A_{\sum\limits_{i\in\mathcal{I}}|\mathcal{N}_{i}|}^{\sum\limits_{i\in\mathcal{ I}}|\mathcal{N}_{i}|}/\prod\limits_{i\in\mathcal{I}}A_{|\mathcal{N}_{i}|}^{| N_{i}|}\).  
Theoretically, one could first enumerate all possible passing sequence combinations, then calculate the delay for each passing sequence, and select the passing sequence with the minimum delay as the optimal one. The number of passing sequence combinations increases exponentially with the number of vehicles. When there are two vehicles in each direction, the number of sequence combinations for vehicles passing through the intersection's conflict zone already reaches 2520. When the number of vehicles is four, the number of passing sequence combinations skyrockets to 63,063,000. This indicates that when the number of vehicles is large, calculating each passing sequence one by one will consume a tremendous amount of computational resources and time. Therefore, the enumeration method is not feasible in practical applications, and it is necessary to construct an optimization model and design an efficient solution algorithm to obtain the optimal passing sequence. The following sections will specifically discuss establishing an optimization model for this problem and designing a solution algorithm.
# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 2 Materials and Methods  
The system flow diagram of the proposed computer-assisted screening of cervical cytology is presented in Figure 2. Our proposed system involved six stages--Image acquisition, image enhancement, cell segmentation, features extraction, features selection, and classification [33]. At the image acquisition step, the SIPaKMeD dataset was used for multi-cells, and the Herlev dataset was used for a single cell. Input Pap smear images were enhanced and denoised to improve the image quality as an image enhancement step. The next step was cell segmentation. This step partitioned the input images into the interesting regions--nucleus and cytoplasm. After segmentation, the next step was feature extraction. In feature extraction, distinctive interested points or features were extracted. In the features selection stage, the random forest algorithm was used as a selection method. The final step was the classification. In this stage, the cells were classified using bagging ensemble classifiers into normal or abnormal cells. The details of each step have been explained in each sub-sections.  
Figure 1: (a) Single-cell image (nucleus and cytoplasm), (b) Multi-cells image, and (c) Overlapping cytoplasm.  
Figure 2: The methodology of proposed cervical cancer detection and classification system.