esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### LONs were Nontoxic to Murine or Human Macrophages at Therapeutic Concentrations  
Introduction of lipophilic moieties into oligonucleotide chains can alter such properties of LONs as cytotoxicity. Cytotoxicity of the most hydrophobic LONs, namely, 2\(\delta\) and 2\(\eta\), was analyzed with respect to murine RAW264.7 and human THP-1-derived macrophages. Cells were incubated in complete media supplemented with different concentrations of one of LONs for 24 h followed by estimation of cell viability using colorimetric MTT assay. As depicted in Figure 3, both 2\(\delta\) and 2\(\eta\) oligonucleotides were nontoxic to murine and human macrophages. IC\({}_{50}\) obtained by extrapolation of dose-response curves ranged from 74 to 120 \(\upmu\)M for 2\(\delta\) and from 77 to 85 \(\upmu\)M for 2\(\eta\) oligonucleotides, whereas the working concentration of LONs in subsequent experiments was chosen to be 5 uM. It is worth mentioning that murine macrophages were more sensitive to LONs in comparison with human THP-1-derived macrophages (IC\({}_{50}\) were 74.2 \(\pm\) 29.6 uM and 120.3 \(\pm\) 31.6 uM for 28 oligonucleotides, respectively; 77.0 \(\pm\) 9.2 uM and 84.6 \(\pm\) 17.2 uM for 2n oligonucleotides, respectively) (Figure 3).
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 4 Experimental Research  
After the construction of the test-rig is completed, experimental research on wheel-rail contact is carried out. First, validations of decoupling theory and slip control accuracy are conducted. Subsequently, adhesion experiments under large slip conditions with different speeds, axle loads and surface contaminants are performed.
## 4 Case Study
### Results from Different Construction Scenarios
#### 4.4.2 Case 2: Formwork Activities  
In the next scenario, a common building situation was described: workers assembling manual formwork structures and placing the slab formwork. Formwork tasks have been highlighted as a source of occupational risks in construction projects, by different authors [70]. FFH was pointed to as the most dangerous risk [68]. Figure 7 illustrates a visual example of this scenario.  
In this example, the worker is manually assembling the formwork structure. They are situated attached to the lifeline and close to the harness and BLE barrier. In the described scenario, the risk value provided by the group of OHS experts is medium. The wind velocity was scored as 30 km/h, the harness was attached and the virtual fence distance was 20 cm. The IoT-JFML system risk score was 5.000014 (Figure 8). This scenario fires Rule 7 with a weight of 100% corresponding to a close BLE barrier being near and the harness detection being connected, so the risk value estimated is medium. Again, this result matched with the results provided by the expert panel.  
Existing technologies could provide alternative solutions to mitigate FFH risk in tasks related to formworks. Some authors propose the combination of BIM and IoT systems [71]; however, continuous monitoring of the worker using visual monitoring system is more expensive that our proposal. In addition the integration of IoT with BIM is complex to install and to configure.
## 3 Our Proposed Method
### Action Classification and Fall Alert  
In the final step of the proposed method, the goal is to classify the action and detect if a human has fallen, as in Figure 2. To accomplish this, the pre-processed images that have undergone human segmentation and image fusion are fed into the previously developed 4S-3 DCNN model that has a 4-branch architecture, as depicted in Figure 6. Each branch within this model handles one image of the four resulting fused images to learn features that capture different spatial and temporal information. The model then classifies every 32 frames of the input video into one of four classes: Standing, Falling, Fallen, or Others, based on the learned features. If there is a sequence of Falling followed by Fallen, the method detects a human fall situation and alerts accordingly.
# Quality-Aware Resource Model Discovery
## 3 Quality-Aware Resource Model Discovery
### Overview
#### 3.1.1 Event Logs  
We utilize a common model of event logs, a collection of process instances, i.e., cases [1]. Every case has a specific trace composed of a series of multiple events, and events can have several attributes, including activities, originators, and timestamps. In addition, the log contains the quality of cases, e.g., yield in production systems, death rates or length of  
Figure 1: Overview of the proposed approach.  
stays in healthcare systems, as a required attribute. It serves as context information for a process. Note that this approach focuses on the quality of business processes, but we can consider other attributes, such as costs or overall time spent on process instances. In that case, we should collect these attributes in the event log. The corresponding formalization is defined in Definition 1.  
**Definition 1**.: _(Event, Attribute, Case, Quality, Event log). Let \(E\) be the event universe. Events may have several attributes (e.g., activity, originator, timestamp). Let \(AN\) be a set of attribute names. For any event \(e\in E\) and any attribute name an \(\in AN:\)\(\beta_{an}(e)\) is the value of the attribute an for event \(e\). If event \(e\) does not have an attribute value, \(\beta_{an}(e)=\bot\) (null value). Let \(C=\{c_{1},c_{2},c_{3},\ldots,c_{k}\}\) be the set of cases. For any case \(c\in C\) and any attribute name an \(\in AN:\)\(\beta_{an}(c)\) is the value of the attribute an for case \(c\). If case \(c\) does not have an attribute value, \(\beta_{an}(c)=\bot\) (null value). Each case has mandatory attributes, i.e., trace (t) and quality (q): \(\beta_{t}(c)\in E^{*}\) and \(\beta_{q}(c)\in\mathbb{R}\). An event log \(L\) is a collection of possible cases._  
Table 1 describes a fragment of the synthetic event log, including context information, i.e., quality. As presented in Definition 1, each case includes several events that have additional information, including activities, originators, and timestamps. For example, CaseID C1 has four different events, and the first event is relevant to Activity A that was performed by Originator M1 and completed at 10:30 on 1 January 2021. In addition, the same quality value, 0.95 is recorded in all events of CaseID C1 as a case attribute.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 4 Experimental Setup
### Gateway  
According to the architecture shown in Figure 4, the ENs must send the information to a GW that serves as a relay between the field data and the NS. Hence, we have selected, programmed, and deployed a GW Dragino LG308 ([[https://www.dragino.com/products/lora-lorawan-gateway/item/140-lg308.html](https://www.dragino.com/products/lora-lorawan-gateway/item/140-lg308.html)]([https://www.dragino.com/products/lora-lorawan-gateway/item/140-lg308.html](https://www.dragino.com/products/lora-lorawan-gateway/item/140-lg308.html)), accessed on 14 December 2022) that incorporates two Semtech radios (SX 1257 and SX1301). Both radios can demodulate signals with a power greater than \(-140\,\mathrm{dBm}\) in 10 different channels simultaneously. Each radio uses a panel antenna (Wilson Electronics ref. 311155 ([[https://www.wilsonamplifiers.com/one-additional-panel-antenna-kit-for-db-pro-311155-k1/](https://www.wilsonamplifiers.com/one-additional-panel-antenna-kit-for-db-pro-311155-k1/)]([https://www.wilsonamplifiers.com/one-additional-panel-antenna-kit-for-db-pro-311155-k1/](https://www.wilsonamplifiers.com/one-additional-panel-antenna-kit-for-db-pro-311155-k1/)), accessed on 14 December 2022)) with a peak gain of 4.4 dBi. Furthermore, we checked the S11 parameter of each antenna  
Figure 6: S11 characterization for an EN. It can be noticed that the antennas used have acceptable behavior regarding the S11 parameter, with returns of less than 3%.  
to get an accurate experimental path loss. Finally, the GW receives the ENs' frames via LoRaWAN radio frequency (RF) and resends them to the TTN via LoRaWAN IP.
# Real-Time Analysis of Facial Expressions for Mood Estimation
## 5 Limitations  
The current method is designed to function under specific acquisition conditions to operate in real-time applications effectively. The user's face needs to be frontal and fully visible within the image, although slight pose variations are tolerated. However, significant deviations may lead to a loss of tracking. While the system can accommodate occasional occlusions, these, along with tracking recovery time, reduce the analysis time and, therefore, can impact the accuracy of mood estimation. Addressing these limitations is a focus for future improvements.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series
### Gaussian Process Regression
The covariance matrix \(K_{\theta}(\vec{t},\vec{t}^{\prime})\) is a positive semidefinite \((n+m)\times(n+m)\) matrix with the following form:  
\[K_{\theta}(\vec{t},\vec{t}^{\prime})= \begin{bmatrix}K_{\theta}(\vec{t},\vec{ t})+\sigma^{2}I&K_{\theta}(\vec{t},\vec{t}^{\prime})\\ K_{\theta}(\vec{t}^{\prime},\vec{t})&K_{\theta}(\vec{t}^{\prime},\vec{t}^{ \prime})\end{bmatrix} ,\]  
where the covariance (sub)matrix \(K_{\theta}(\vec{a},\vec{b})\) is defined as \(K_{\theta}(\vec{a},\vec{b})=(k_{\theta}(a_{i},b_{j}))_{a_{j}\in\mathbb{Z},b_{ j}\in\vec{b}}\). Conditioning on \(X\) and its posterior distribution \(\mathcal{N}(0,k_{\theta}+\sigma^{2})\), we obtain the Gaussian predictive distribution of \(\vec{x}^{\prime}\). Specifically, we obtain that the values of the data points \(\vec{x}^{\prime}\) follow a \(m\)-variate Gaussian distribution with mean value vector \(\vec{m}^{\prime}\) and covariance matrix \(K^{\prime}\) as given below:  
\[\vec{m}^{\prime} =K_{\theta}(\vec{t}^{\prime},\vec{t})\,(K_{\theta}(\vec{t},\vec{ t})+\sigma^{2}I)^{-1}\vec{x} \tag{13}\] \[K^{\prime} =K_{\theta}(\vec{t}^{\prime},\vec{t}^{\prime})-K_{\theta}(\vec{t }^{\prime},\vec{t})\,(K_{\theta}(\vec{t},\vec{t})+\sigma^{2}I)^{-1}K_{\theta} (\vec{t},\vec{t}^{\prime}) \tag{14}\]  
We note that the predicted mean value of \(\vec{x}^{\prime}\) in (13) is a linear combination of the input values \(\vec{x}\). Equivalently, one can obtain the mean value of each \(x^{\prime}_{j}\) as a linear combination \(\sum_{i=1}^{n}\alpha_{j}k_{\theta}(t_{i},t^{\prime}_{j})\), with coefficients \(\vec{\alpha}=(K_{\theta}(\vec{t},\vec{t})+\sigma^{2}I)^{-1}\vec{x}\). We also note that the covariance matrix in (14) does not directly depend on the input values \(\vec{x}\) (but it depends on the points in time \(\vec{t}\) when these values are observed). We refer the interested reader to ([14], Section 2.2) for more details on Gaussian Process Regression.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 3: Multi-Scale Input
#### City- and Regional Scales  
Due to the considerable spatial heterogeneity of urban forests across large areas, regional analysis of urban forests and associated ecosystem services using remote sensing techniques is still rarely conducted. Nevertheless, urban tree canopy cover in Los Angeles and Sacramento, California was mapped using QuickBird satellite and aerial imagery [82]. Combined with field survey data (e.g., age and species composition of urban trees), carbon storage and sequestration density were quantified across the study region.  
At the national level, Landsat-5 (TM) and Landsat-7 (ETM+) SLC-on images with a processing level at Level 1T of 30 Chinese cities in 1990, 2000, and 2010 (obtained from the Earth Resources Observation and Science Center, USGS) were used to investigate changes in urban green spaces [77]. Combined with socioeconomic and natural data, the potential driving forces behind the identified changes were then explored.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Tele-Operated Driving
#### 3.1.3 What Are the Benefits of 5G Compared to 4G for Tele-Operated Driving (ToD)?  
ToD has demanding requirements with respect to functional safety as errors generated by the automated vehicle system might cause injuries to passengers and other road users. Today's concepts for functional safety, like the one specified mainly in the ISO26262 standard, are not considering the case that vital parts of the system are developed without considering the rules specified in the ISO26262. To keep the possibility to provide functional, safe ToD, concepts have to be developed that allow the existence of system elements that are not developed according to ISO26262 while still maintaining functional safety under full control (this could be but is not limited to a safe control of the respective system elements). Functional safety and reliable end-to-end (E2E) QoS communication requirements are key needs. Cross-border operations impose additional, significant challenges for lag-free data transmission when handing over between MNOs.  
Furthermore, a high quality of the information provided to the tele-operator, e.g., from cameras in the vehicle, is necessary to allow safe tele-operation. Camera information, together with data from other sensors, must be provided to the tele-operator with shortest possible latency, high quality and update rates. Latency of the 4G/LTE mobile networks can be inconsistent and may peak up to multiple hundreds of milliseconds. Thus, in order to avoid jitter in video streams, it is necessary to introduce a buffer. This adds to the age of the information that is displayed to the tele-operator. As will be discussed in Section 4.1, the application of 5G solutions, such as QoS prediction or network slicing, contribute to improved functionality of the ToD technology.
## 3 Results
#### 3.2.2 Zeta Potential Analysis  
Zeta potential indicates the surface charge of particles in a solution, and it measures the repulsion or attraction of charges between particles. Thus, smaller particles with higher zeta potential confer good particle stability and resist particle aggregation [27]. According to previous reports, NPs with zeta potential values \(>+25\) mV or \(<-25\) mV have high degrees of particle stability [28]. In the current study, blank PEG-b-PLGA NPs had a zeta potential value of \(-39.1\pm 1.05\) mV, and PEG-b-PLGA NPs encapsulated with geranin had a zeta potential of -\(40.8\pm 0.90\) mV. Thus, both values were \(<-25\) mV, which indicated the negative surface charge and stability of the NPs. NP surface charge represents another design feature that can be tailored to prolong circulation lifetimes of the NPs and selectively enhance the accumulation of NPs at specific sites of interest. NPs with neutral and negative surface charges have been shown to resist the adsorption of serum proteins, resulting in longer circulation half-lives [29].
# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 2 Materials and Methods
### Overall Framework  
Our model design is based on an encoder-decoder structure. The encoding path mainly consists of several residual blocks. In contrast, the decoding path is divided into two branches to up-sample the feature maps. The first branch is the main decoding path, and the second branch is the intermediate branch. The main decoding path uses a traditional up-sampling process. In particular, the structure of the intermediate branch is similar to that of the main decoding path, but their inputs are different. The intermediate branch reuses the features in the encoder and uses the loss function for backpropagation to optimize the parameters. Inspired by self-supervision learning [26], we add the hybrid-loss function in the network. Specifically, we not only utilize the feature map of the last layer of the decoder to predict landmarks but also use the feature map of the last layer of the intermediate branch to assist in prediction. On this basis, our primary decoder uses the feature information learned by the intermediate branch to capture more representative features and to reduce the acquisition of invalid information from the encoder.  
A prediction module is utilized to detect landmarks of the spine. This module can generate a heatmap, an offset map of center points, and a centripetal vector map. Through the arithmetic addition between the heatmap and the offset map, we obtain the coordinates of the center points. Our goal is to obtain landmarks, so we use the coordinates of the center points to subtract the centripetal vector to obtain the set of corner coordinates. Afterward, the Cobb angle can be obtained by a geometric algorithm.  
In addition, we merge the feature maps of different layers of the main decoding path to obtain the fusion feature map. Feature fusion can optimize the model's parameters and improve landmark detection accuracy, leading to a precise Cobb angle. The overall framework of our method is shown in Figure 2.  
We also wondered which hybrid structure would lead to better performance. Therefore, we explored the impact of introducing a transformer earlier in the encoder-decoder network on the final model. In other words, we removed residual module 1 in the encoder-decoder (Shown in Figure 2), which resulted in a larger size of feature maps processed by the transformer. We compare the performance of the two models in Section 3.4.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Electrical Test  
#### 4.1.1 The Maximum Counting Rate  
The maximum rate for the counter was directly measured using a narrow square pulse with a duty cycle of 50%. We checked the measured rate from Cosmo ArduSiPM counters and, at the same time, the TTL_Out signal from the board and the output of the comparator stage. Compatible with the SAMV71 clock, a maximum frequency of 80 MHz is possible for internal counters. The outcome of the test is a maximum supported rate of 42 MHz. The study of comparator stage output highlights dependence on its setting time for this limit (Figure 7). In the standard operativity of the detector, the maximum measurable rate is affected by the SiPM pulse width, usually greater than 25 ns (40 MHz).
# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 3 Approach
### Recurrent Neural Network (RNN)  
RNN is a type of ANN in which the connections between units produce a directed graph along a sequence. This architecture enables the network to demonstrate dynamic temporal behavior for a time sequence. Contrary to feedforward neural networks (FFNNs), RNNs can process sequences of inputs by using their internal state (memory), which makes them appropriate to many NLP tasks [17; 18; 19]. LSTM is a type of RNN architecture that is the best among all other existing architectures [20]. RNNs are deep learning systems that stem from the modifications of FFNN with recurrent connections. In a typical neural network, the output of a neuron at time t is calculated as  
\[y_{i}^{t}=\sigma(W_{i}x_{t}+b_{i}), \tag{1}\]  
where \(W_{i}\) is the weight matrix, and \(b_{i}\) is a bias term. In RNN, the calculation of the activation function is modified because the output of the neuron at the time '\(t-1\)' is fed back into the neuron. Then, the new activation function can be computed as  
\[y_{i}^{t}=\sigma\Big{(}W_{i}x_{t}+U_{i}y_{i}^{t-1}+b_{i}\Big{)}. \tag{2}\]  
RNNs can remember previous information of a sequence by using the output of the earlier node as recurrent connections while presenting output depending on the former states. This characteristic makes the network useful for the sequence labeling task. The backpropagating error can regularly \"strike up\" or blast, which yields infeasible convergence. It can also vanish to render the capability of the network to learn long-term dependencies, which are difficult to learn through gradient descent.
# Searching via Nonlinear Quantum Walk on the 2D-Grid
## 2 Model
### Adding a Nonlinearity
\right), \tag{29}\]  
with  
\[\delta(t)=\left(\frac{|a|^{2}}{4}-\frac{4|\beta|^{2}}{N}\right). \tag{30}\]  
The eingenvalues of the above matrix are :  
\[E^{\prime}_{\pm}=\frac{1}{2}g\delta\pm\frac{1}{2}\sqrt{(g\delta)^{2}+4E^{2}} \tag{31}\]  
with eigenstates, respectively:  
\[|\psi^{\prime}_{\pm}\rangle=(\frac{g\delta}{2E}\pm\frac{\sqrt{(g\delta)^{2}+4E ^{2}}}{2E},1). \tag{32}\]  
Notice that, if the perturbation \(g\delta\) is sufficiently small and we rescale the spectrum, we can recover the eigenstates of the form \((\pm 1,1)\) and thus, force the system to oscillate between them, similarly to the linear case. Indeed, let us multiply the Hamiltonian \(H_{L}\) by a term \((1+c(N)g\delta)\), with \(c(N)\) being a real function of \(N\). The eigenvalue \(E\) now transforms as follows:  
\[E\to E(1+c(N)g\delta). \tag{33}\]  
In order to make the eigenstates (32) approximately of the form \((\pm 1,1)\), we have to impose \(2E(1+c(N)g\delta)\gg g\delta\) or equivalently  
\[c(N)\gg\frac{1}{2E}-\frac{1}{g\delta}. \tag{34}\]Moreover, an upper bound of \(c(N)\) can be obtained as follows:  
\[\delta(0)=\frac{|a(0)|^{2}}{4}-\frac{4|\beta(0)|^{2}}{N}=-4/N \tag{35}\]  
since \(|\psi(t=0)\rangle=|s\rangle\) and \(|\beta(t)|^{2}=|\left\langle s\right\rangle\psi(t)|^{2}\). Thus,  
\[c(N)<c_{max}=\frac{N}{4g}. \tag{36}\]  
Finally, keeping only the leading term \(1/2E\) in Equation (34), we can bound \(c(N)\) as follows  
\[\frac{N}{4g}>c(N)\gg\frac{1}{2}\sqrt{\frac{N\log N}{16\pi}}=\frac{1}{2E}. \tag{37}\]  
Notice that the above inequality implies that \(g\) cannot scale faster than \(\sqrt{N}\). Finally, we can summarise the nonlinear Algorithm 2 as follows:  
```
Initialization: \(|\psi\rangle=|s\rangle\); \(t=0\); while\(t<T^{\prime}\)do \(|\psi\rangle=\exp\left(-i\Delta t\left(\left(1+gc\delta\right)H_{L}+H_{NL} \right)\right)|\psi\rangle\); \(t=t+\Delta t\); end  Measure \(|\psi\rangle\)
```  
**Algorithm 2**Searching with a nonlinear algorithm for the \(2d\)-grid  
Contrary to the linear Algorithm 1, an exact analytical treatment to explicitly calculate \(T^{\prime}\), for the Algorithm 2, is difficult. In the following, first, we will provide strong numerical evidence that such a search scheme allows a clear temporal advantage over the linear case, deriving numerically \(T^{\prime}\) and second, we will give an approximate analytical proof for it. In conclusion, we will discuss the time measurement precision and how it affects the choice of the parameters.