esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Assistance Protocol at Stop Sign Intersections
#### 4.2.3 Passing or Waiting Decision  
Stopping vehicles at the road intersection need to decide when to traverse the road intersection based on their priorities to move through it. Upon stopping at the targeted intersection, the first stopping vehicle \(v\) has to capture its arriving time \(Ar_{time}\). The first stopping vehicle in each input flow of the intersection keeps the \(Ar_{time}\) until it traverses the road intersection. Then, the next arrival vehicle sets the current \(Ar_{time}\). In order to traverse through the shared road intersection, each vehicle \(v\) stopping at the controlled road intersection has to check all the competing traffic flows. If any competing traffic flows are the main flow (i.e., has no stop sign), the vehicle has to stop waiting and sensing other vehicles on that main flow. This includes gathering the real-time location (\(L\)) and speed (\(S\)) of vehicles moving on the main traffic flow. Based on analyzing the locations and speeds of vehicles there, the gap distances between successive vehicles are determined using Equation (2). Where \(L_{0}\) is the location of the road intersection, \(L_{1}\) is the location of the first vehicle (\(V_{f}\)) on the main flow, \(L_{2}\) is the location of the second vehicle (\(V_{s}\)) on the main flow, \(S_{1}\) is the speed \(V_{f}\), and \(S_{2}\) is the speed of \(V_{s}\). When a safe gap (i.e., the difference between the arrival times to the intersection of two successive vehicles is enough for a vehicle to traverse the road intersection safely) is detected on the main flow, the first arrival-conflicted vehicle can proceed through the road intersection. Figure 4 illustrates an example of a four-leg road intersection, where a secondary road crosses the main road scenario. In this figure, the safe gap between two successive vehicles is illustrated by a red bounded box.  
\[gap=\frac{L_{2}-L_{0}}{S_{2}}-\frac{L_{1}-L_{0}}{S_{1}} \tag{2}\]  
On the other hand, if the stop signs are installed on all input traffic flows of the road intersection, each vehicle \(V\) that arrives at the road intersection should save and announces its arrival time \(Ar_{time}\). Then, each vehicle checks its competing traffic flow to find the \(Ar_{time}\) of the first vehicle in each flow. The first arrival vehicle has the highest priority to traverse the road intersection first.  
Figure 4: Safe gap between two successive vehicles in the main road scenario.
## 2 Materials and Methods
### Neural Network Architecture
#### 2.2.2 Layer-Wise Breakdown  
1. Input layer specifications--neuron count: The input layer comprises 22 neurons, each designed to process specific elements of the incoming data.  
Figure 1: Process flow charts. (**a**) The neural network optimization process using Slime Mold Optimization (SMO). (**b**) The environmental interaction process for terrain navigation and obstacle avoidance.  
* Functional overview: The primary function of this input layer is to assimilate raw data, which, in the context of this study, encompasses a variety of environmental and operational parameters encountered by the UAV.
* Input parameters: The layer receives the following inputs, each representing critical aspects of the UAV's operational environment: 1. Pitch: Reflecting the UAV's angular orientation along the lateral axis. 2. Roll: Indicating the UAV's angular orientation along the longitudinal axis. 3. Yaw: Representing the UAV's rotation around the vertical axis. 4. Position (x, y, z): Spatial coordinates capturing the UAV's location in three-dimensional space. 5. Span: Denoting the UAV's wingspan or a similar dimensional measurement relevant to its structure. 6. Net force in y and z: Representing the total forces acting on the UAV in the vertical and lateral planes. 7. Relative sunlight angle: Indicating the angle of sunlight relative to the UAV's orientation, crucial for solar-powered operations. 8. Reduction of sunlight intensity due to clouds: Quantifying the decrease in solar energy availability due to cloud coverage. 9. Time of day (azimuthal angle): Reflecting the sun's position, essential for understanding diurnal variations in environmental conditions [43]. 10. Current battery capacity: Indicating the UAV's available energy resources at any given moment. 11. Maximum battery capacity: Denoting the UAV's total energy storage capability. 12. Motor power and motor torque: Providing insights into the UAV's propulsion system's current operational status. 13. Velocity (x, y, z): Capturing the UAV's speed across three dimensions. 14. Closest object in x, y, z coordinates: Identifying the proximal objects in the UAV's immediate vicinity, critical for collision avoidance and navigational purposes.  
These parameters collectively formed a comprehensive dataset, facilitating the neural network's initial processing phase and laying the foundation for complex, subsequent analytical processes.
## 3 Surface Defect Detection Network for Automobile Wheel Hub
### Decoder Module
#### 3.4.3 Final Feature Merging Branch  
First, the final feature merging branch adds \(F_{body}\) and \(F_{edge}\) to obtain the context feature, which contains body and edge information. Then, the context feature is concatenated with \(F_{multi}\), which can introduce the correlation between \(F_{body}\) and \(F_{edge}\) lost when decoupling and effectively aggregate \(F_{body}\) and \(F_{edge}\). Next, two 3 \(\times\) 3 convolutions are used for feature extraction to obtain the final feature (\(F_{final}\)). A 1 \(\times\) 1 convolution is used to extract global information and reduce the number of channels to two. Finally, double upsampling and sigmoid are used to obtain the final segmentation map (\(s_{final}\)). Similar to Equation (5), weighted cross entropy loss is used to calculate the loss (\(L_{final}\)) of \(F_{final}\) according to \(s_{final}\) and label \(\hat{s}\):  
\[L_{final}=-\frac{1}{N}\sum_{i=1}^{N}\omega_{c}\cdot\left(y_{final,i}log(p_{ final,i})+\left(1-y_{final,i}\right)log(1-p_{final,i})\right) \tag{8}\]  
To supervise \(F_{final}\), \(F_{body}\), and \(F_{edge}\) simultaneously, the losses from the body feature optimization, edge feature optimization, and final feature merging branches are combined as the total loss (\(L\)) of the entire network. \(L\) can be formulated as Equation (9).  
\[L=\lambda_{final}L_{final}+\lambda_{body}L_{body}+\lambda_{edge}L_{edge} \tag{9}\]  
\(\lambda\) refers to the weight of each loss, which is set to 1 by default.
## 2 Results and Discussion
### Erosion Studies of Hydrogels  
Since each biomaterial should be safe for humans and the environment after completion of therapy, we decided to investigate the kinetics of decomposition of the obtained hydrogels. The erosion test of the CL-PEG C hydrogel obtained without an active substance (H-CON) was carried out. The test of the resulting hydrogels was conducted under the same conditions as the CBD and/or TER release experiments. The erosion process was characterized by plotting the hydrogel's weight loss (_WL_) versus time (Figure 4). It was found that the erosion rate of H-CON depends on the pH of the solution. Hydrogels degraded faster at pH 5.5 than at pH 7.4, which was consistent with the CBD and/or TER release experiment data.  
The _WL_ value for H-CON was 77.0% at pH 5.5 after 8 weeks of erosion testing. In turn, the _WL_ value for H-CON was 68.0% at pH 7.4 after 8 weeks of degradation. The results of the tests performed over 8 weeks are satisfactory. Generally, the erosion process of the obtained hydrogels was relatively regular.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Zero Order Model** & **First Order Model** & **Korsmeyer–Peppas Model** & **Transport Mechanism** \\ \hline  & \(R^{2}\) & \(R^{2}\) & \(R^{2}\) & \(n\) & \\ \hline H-CBD \({}^{\mathrm{a}}\) & 0.927 & 0.947 & 0.827 & 0.503 & non-Fickian transport \\ H-TER \({}^{\mathrm{a}}\) & 0.883 & 0.932 & 0.858 & 0.412 & Fickian transport \\ CBD (H-CBD-TER) \({}^{\mathrm{a}}\) & 0.931 & 0.972 & 0.845 & 0.511 & non-Fickian transport \\ TER (H-CBD-TER) \({}^{\mathrm{a}}\) & 0.915 & 0.859 & 0.780 & 0.435 & Fickian transport \\ \hline H-CBD \({}^{\mathrm{b}}\) & 0.905 & 0.918 & 0.775 & 0.383 & Fickian transport \\ H-TER \({}^{\mathrm{b}}\) & 0.770 & 0.971 & 0.793 & 0.292 & Fickian transport \\ CBD (H-CBD-TER) \({}^{\mathrm{b}}\) & 0.910 & 0.933 & 0.771 & 0.404 & Fickian transport \\ TER (H-CBD-TER) \({}^{\mathrm{b}}\) & 0.832 & 0.897 & 0.788 & 0.317 & Fickian transport \\ \hline \end{tabular} \({}^{\mathrm{a}}\) pH 7.4, \({}^{\mathrm{b}}\) pH 5.5.  
\end{table}
Table 2: Analysis of CBD and TER release results from obtained hydrogels using mathematical models.
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 3 Formalizing Risk Mitigation
### Transitions and Emergency Response  
By ensuring that \(M_{C_{0},C_{1}}(t)\in\mathcal{C}\;\forall t\in[0,1]\), we are acknowledging the fact that each intermediate stage of the transition is a configuration in and on itself, potentially with its own associated risk assessment. While this may not seem to be particularly relevant for long-term risk assessment (unless the material time to complete the transition is comparable with the expected occurrence timescale of the hazardous events), the significance of this formulation becomes evident when considering its application to emergency responses.  
Consider a small-scale example such as a single building and its fire hazard. During an emergency (one or more fires have started), the response might include an evacuation plan that, in our formulation, maps to a _transition_ from an initial configuration \(C_{0}\) (in which people are distributed e.g., to their habitual workplaces within the building) to a new configuration \(C_{1}\) in which no people are left in the building. The transition itself will involve, at every instant in time, a new distribution of people in the building, along the established evacuation routes. However, the choice of the evacuation routes (i.e., the choice of transition \(M_{C_{0},C_{1}}\)) has an impact on the inherent risk associated _with the transition itself_, due to the different distribution of people along them at each moment during the evacuation.  
Note also that, in such a case, the pointwise risk function \(r\) itself would not be fixed, so a more sophisticated formulation that takes this into account would be necessary to complete the mathematical formulation necessary for the design of the emergency response.
## 4 Atypical Hemolytic Uremic Syndrome (Non-EHEC)  
HUS not due to enteropathogenic bacterial infection is known as atypical HUS (aHUS) and there are many triggers besides infection, with the underlying cause being genetic deficiencies in regulators of the alternative pathway of complement [88]. In this case, the alternative pathway of complement is a fundamental initiator of disease. aHUS leads to recurring bouts of HUS, and can lead to stroke, heart complications and end-stage renal failure, although how quickly this progresses depends on the nature of mutation in the complement pathway members [89]. Histologically, aHUS is indistinguishable from D + HUS in the kidney. A majority (60%-70%) of patients with cofactor H (CFH) mutations progressed to end-stage renal failure within a year of aHUS symptoms [90]. Many children experience their first bout of aHUS before age 6 months, which can help lead a differential diagnosis towards aHUS instead of D + HUS [91]. Treatment with Eculizumab (Soliris(r)), a humanized monoclonal antibody against complement C5, has had success in aHUS patients because it blocks the rampant, dysregulated activation of C5 thereby preventing formation of inflammatory C5a and the cytotoxic terminal attack complex C5b-9 [85,92].  
Atypical HUS can be sporadic or familial, and is associated with dysregulation of complement typically characterized either by over-activation or compromised regulators of the alternative pathway of complement. Ordinarily, complement factor H (CFH), along with complement factor I (CFI) and membrane cofactor protein (MCP) accelerates the decay of C3 convertase, inhibiting complement activation. Thromomodulin, an endothelial cell surface membrane protein usually associated with anti-coagulant function [62], aids this enzymatic complex in its activity. Mutations in thrombomodulin [93] as well as CFH [94], CFI [90], and MCP [95] are all implicated as risk factors for development of aHUS, along with mutations in complement factor B [96] and C3 [97]. In addition, autoantibodies against CFH have also been demonstrated [89]. While infection can be a possible trigger for the manifestation of aHUS, in general there is much less evidence of leukocytes and inflammation in patients with aHUS than in those with D + HUS [53]. Yet, there may be overlap and at least one patient with EHEC-HUS has been shown to also have a mutation in MCP that would confer risk of developing aHUS [98], emphasizing how the lines between these syndromes can be blurred.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 2 Methodologies
### The Evaluation Principles of PPP-B2b Precision Products  
Because there are some inconsistencies between the PPP-B2b products and the GBM final products, these inconsistencies need to be corrected using the following methods before a comparison can be made.  
The satellite positions calculated with the PPP-B2b corrections use the antenna phase center (APC) as the reference point, while the GBM orbit uses the center of mass (CoM) as its reference point [15; 16]. The difference between the reference points in these two types of orbit products needs to be eliminated.  
The BDS-3 satellite's clock offsets in the B2b products are in reference to the B3I signal, which differ from the GBM final clock offsets estimated based on the ionosphere-free linear combination (IFLC) of the B1I/B3I measurements. This difference can be corrected using the differential code bias (DCB) parameters in the B2b product. In addition, the B2b clock products and the GBM final clock products use different APC correction values [14; 17]. The difference between these two types of clock products needs to be eliminated. A double-difference (DD) approach is usually applied for satellite performance evaluation to eliminate the time scale difference between different clock products. In addition, the PPP-B2b products are estimated based on a regional ground observation network. This will lead to a variation in the number of satellites and cause discontinuities in the calculated DD clock offset time series [18].
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Nature of the Data  
The selection of the anomaly detection technique depends on the nature of the data being analyzed. The Internet of Things (IoT) is a major source of data streams. A data stream is a continuous series of data records followed and arranged by implicit or explicit timestamps [14]. This data sequence has three key features. First, it is a stream of continuous data flow. Hence, the algorithm should process the data in a limited time. Second, the data stream is unlimited. In other words, the number of data points entering is infinite. Thus, storing such a massive volume of data is another major obstacle. Lastly, data streams change over time, i.e., evolve [16]. Detecting anomalies in the data stream has practical and important applications across a wide range of fields [15]. In realistic implementations, the significance of detecting anomalies in the data stream accelerates its growth by ensuring precision and immediacy [15]. Several methods have been used to propose learning algorithms for anomaly detection in data streams such as C-LOF [14], AutoCloud [24], TEDA Clustering [25], Evolving spiking neural network [43], Combination of (BDLMs) & (RBPF) [26], KPI-TSAD [40], HTM [27], ensembles neural networks [41], Multiple kernel learning [30], xStream [31], CEDAS [36], MuDi-Stream, [37], Long Short-Term Memory [15,45], Density-based Clustering [46], and others.
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 2 Materials and Methods
### Reservoir Rock Sample Electromagnetic Properties  
The magnetic property of the sandstone samples was characterized using a vibrating sample magnetometer (VSM GMW 7400, Lakeshore, Westerville, OH, USA). For the magnetic response measurement, the VSM experiment was conducted applying a maximum magnetic field of 1500 G on the magnetite nanoparticle and sandstone samples. The hysteresis loop, magnetization, coercivity and retentivity of the samples were obtained. The dielectric permittivity of the samples was measured within a frequency range of 1 MHz to 4 GHz using a vector network analyzer (ENA E5071C). The frequency-dependent conductivity \(\sigma_{\text{ac}}\) (S/m) was computed from the measured complex relative permittivity value using Equation (4).  
\[\sigma_{\text{ac}}=2\pi f\varepsilon^{\prime\prime}\varepsilon_{0} \tag{4}\]  
where f is the frequency, \(\varepsilon^{\prime\prime}\) is the imaginary part of permittivity and \(\varepsilon_{0}\) is the permittivity of free space.
## 2 Technology Background
### K-Means Clustering  
K-means algorithm is a typical clustering algorithm based on distance. This algorithm adopts distance as the evaluation parameter of similarity, which means the shorter the distance is the more  
Figure 2: Schematic of the RGB color model (**Left**) and HSI color model (**Right**).  
similar the two objects are. K-means clustering aims to partition \(n\) observations into \(k\) clusters. Each observation in k-means clustering belongs to the cluster with the nearest mean, which serves as the prototype of the cluster. We take advantage of this feature to partition \(\mathtt{M}\times\mathtt{N}\) pixels in the stained blood smear image into several clusters on the basis of the color information of the background, RBCs, cytoplasm, and white cell nuclei. M denotes the number of rows in the image, and N denotes the number of columns in the image.  
In the clustering problem presented in this paper, the training set consisted of each pixel in the image and was denoted as \(\left\{x^{(1)},x^{(2)},...,x^{(L)}\right\}\), where \(x^{(l)}\in\mathbb{R}^{n}\), \(\mathtt{i}\in\left[1\;\mathtt{L}\right]\), \(\mathtt{L}=\mathtt{M}\times\mathtt{N}\). Each sample \(x^{(l)}\) of the training set is a vector decided by the combination of color components of different color spaces. The adopted algorithm is described by the following steps:  
1. Select an initial set of k-cluster centroids \(\mu_{1},\mu_{2},...,\mu_{k}\in\mathbb{R}^{n}\) randomly.
2. After assigning the initial k-cluster centroids, the algorithm proceeds by alternating between two steps until the steps converge:  
**Assignment step:** For each sample \(x^{(l)}\), decide the cluster of a sample by calculating the squared Euclidean distance between sample \(x^{(l)}\) and each cluster centroid \(\mu_{j}\) (\(\mathtt{j}\in\left[1\;\mathtt{k}\right]\)) according to Equation (3):  
\[c^{(l)}\coloneqq\arg min\big{\|}x^{(l)}-\mu_{j}\big{\|}^{2}, \tag{3}\]  
where \(c^{(l)}\) denotes the cluster number of \(x^{(l)}\).  
**Update step:** For each cluster, recalculate the new means to be the centroids of the observations in the new clusters according to Equation (4):  
\[\mu_{j}\coloneqq\frac{\sum_{l=1}^{L}\mathtt{i}\{c^{(l)}=f\}x^{(l)}}{\sum_{l=1 }^{L}\mathtt{i}\{c^{(l)}=f\}}. \tag{4}\]
## 3 Local Differential Privacy
* Adaptive Synthesis Process: The framework builds a probabilistic model using these extracted features, allowing it to generate synthetic trajectories that mimic real movement patterns [17]. The synthesis process is adaptive, meaning it selects transitions and trajectory lengths based on learned distributions without needing exact user data, thus enhancing privacy. Trajectory synthesis under local differential privacy ensures that user mobility patterns can be anonymized without sacrificing their statistical utility. In [3], a Markov chain-based model is used to probabilistically synthesize trajectories. In [9], a graph-based model offers the possibility of using more flexible grids. LDPTrace models transitions between regions, as well as the distribution of start and end points, to replicate realistic mobility behavior. By utilizing these probabilistic distributions, LDPTrace preserves the overall structure of the mobility data while effectively anonymizing individual details. LDPTrace offers several benefits over traditional trajectory anonymization methods:
* Enhanced Privacy Protection: By employing local differential privacy, LDPTrace reduces the risk associated with data aggregation and central storage, ensuring that users' real mobility data is not exposed [4]. * Improved Utility: The framework maintains high data utility, as synthetic trajectories generated by LDPTrace closely match real-world movement patterns [4]. This is particularly beneficial for analyses that depend on aggregated mobility trends rather than specific individual behaviors. * Low Computational Cost: Unlike older methods that rely on intensive computations, such as linear programming or external data integration, LDPTrace simplifies the synthesis process, making it feasible for use on devices with limited resources [4]. * Resistance to Attacks: The method is designed to withstand common location-based attacks, such as reidentification and outlier analysis. By generating trajectories that do not closely mirror any specific user data, LDPTrace effectively minimizes vulnerabilities [4]. In LDPTrace, most of the computations on individual trajectories are performed by the individual user's edge device. The computational load on the aggregation center, where the trajectory synthesis is performed, is therefore reduced. The total computational complexity on each edge device for a trajectory \(T\) is \(O(|T||C|)\), which consists of \(O(|T|)\) for perturbing the length vector and start/end points and \(O(|T||C|)\) for perturbing the grid region transitions where \(|C|\) is the number of grid regions. Performing LDPTrace on an edge device with limited resources should therefore be possible. Further runtimeexperiments for LDPTrace are published in [4], showing that LDPTrace performs better than other approaches of LDP.
# Effect of Substrates on the Dynamic Properties of Inkjet-Printed Ag Thin Films
###### Abstract  
The dynamic properties of inkjet-printed Ag thin films on flexible substrates were measured using flexural wave propagation. The Ag nanoparticle suspension was inkjet-printed on polyimide (PI), silicon wafer, and glass. The effects of flexible substrates on the dynamic properties of the films were investigated. Beam-shaped Ag-printed substrates were fabricated by pico-second laser pulse cutting. The wave approach was presented to analyze the vibrations of the thin film on the substrates. The Young's modulus and loss factor of the Ag thin films with the substrates were represented by the combined bending stiffness of the bilayer beam. The vibration response of the base-excited cantilever was measured using an accelerometer and laser Doppler vibrometer (LDV). Vibration transfers were analyzed to obtain dynamic characteristics of the Ag-printed bilayer beam. The substrate affects the reduction of the Ag thin film thickness during the sintering process and surface roughness of the film. The proposed method based on the wave approach allows measurement of the dynamic properties regardless of the ratio of the modulus between the thin film and substrate.  
Keywords: inkjet printing; thin-film; dynamic property; flexural wave; cantilever +
Footnote †: journal: Applied Physics Letters