esa-sceva/satcom-chunk-collection huggingface dataset:
# MFCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
## 3 Model  
### Problem Formulation  
The objective of the federated personalized cross-domain recommendation model based on meta-learning is to forecast the ratings of goods by cold-start users in the target domain. There is a source domain and a target domain, each with a user set \(U=\{u_{1},u_{2},\ldots u_{M}\}\), and an item set \(T=\{t_{1},t_{2},\ldots t_{N}\}\), and a rating matrix \(R\), where M and N are, respectively, the number of users and items. For ease of differentiation, we denote the source domain user set, item set, and rating matrix as \(U^{s}\), \(T^{s}\), \(R^{s}\), where \(R^{s}=y^{s}_{i,j}\) denotes a user's rating of an item, and the user set, item set, and rating matrix in the target domain as \(U^{l}\), \(T^{l}\), \(R^{l}\). We define the overlapping users between the two domains as \(U^{l}=U^{s}\cap U^{l}\), and there are no shared items between the two domains, and therefore \(T^{s}\) and \(T^{t}\) do not intersect.  
We convert users and items into embeddings, where the embedding of user \(u^{d}_{i}\) and item \(t^{d}_{i}\) is denoted as \(p^{d}_{u}\in R^{k}\) and \(q^{d}_{v}\in R^{k}\), where \(k\) represents the dimension of embedding and \(d\in\{s,t\}\) is the domain label. For each user \(u_{i}\), the series of sequential interactions in the origin is given as \(S_{u_{i}}=\left\{t^{s}_{1},t^{s}_{2},\ldots t^{s}_{n}\right\}\), where N is the number of interaction items and N is less than M. \(U^{c}\) is represented as only users who have ratings in \(U^{s}\), using \(U^{l}\) as a vehicle for knowledge transfer from the source domain to the target domain, and therefore forecasting the target domain's \(U^{c}\)'s rating. Let us take a real-world example to make the problem we are solving more understandable. We want to build a book recommendation system to recommend books of interest to users. However, we do not have a user's book interest dataset; we only have a user's music interest dataset, and there are some overlapping users in the user's book interest dataset and the user's music interest dataset. We take the user's music interest dataset as the source domain and the book interest dataset as the target domain. Both the music interest dataset and the book interest dataset have a user set, an item set, and a rating matrix. An overlapping user is a user who exists in both the music interest dataset and the book interest dataset but only has a rating for the music interest dataset. We need to use the overlapping users' ratings on the music interest data to learn the preferences of the overlapping users, and then we can use the overlapping users' preferences to make recommendations to them in the book interest data set, thus solving the cold-start problem. This is similar to shopping with Amazon: after we have browsed some books that we are interested in, Amazon will recommend other items that are related to our interests.
## 3 System Model
### Joint Placement Metrics  
In the previous subsection, we briefly introduced the joint placement problem for network reliability maximization with latency constraints in SDN-enabled ISTNs. Here, we formulate the problem and the performance metrics mathematically.  
For any switch \(u\in\mathcal{U},L_{uw}^{s}\) denotes the latency of the path from switch \(u\) to the satellite \(s\), via gateway \(w\), which can be defined as:  
\[L_{uw}^{s}=L_{uw}+L_{sw}, \tag{1}\]  
where \(L_{uw}\) and \(L_{sw}\) denote the latency from switch \(u\) to gateway \(w\) and the latency from \(w\) to the satellite \(s\), respectively. Since the distance between the GEO satellite and the ground can be considered as fixed while all data traffic transmission between the switches and the satellite has to pass through gateways, the latency between switches and gateways plays a principal role. Thus, the average network latency can be formulated as:  
\[L_{avg}=\frac{1}{n}\sum_{u\in\mathcal{U}}L_{uw},\quad w\in\mathcal{W}. \tag{2}\]  
Once the satellite gateway placement problem is established, the average network latency of the integrated networks will be determined.  
Besides, the average and maximum propagation controller-switch latency can be defined respectively as:  
\[L_{uc,avg}=\frac{1}{n}\sum_{u\in\mathcal{U},c\in\mathcal{C}}L_{uc}, \tag{3}\]  
\[L_{uc,max}=\max_{u\in\mathcal{U},c\in\mathcal{C}}L_{uc}. \tag{4}\]  
Owing to the influence of geographical locations and the distribution of the nodes, different placements of controllers can lead to different network reliability performances, while different placements of gateways influence both network latency and reliability to varying degrees.  
For the set of terrestrial nodes \(\mathcal{U}\), we defined \(P_{u}\), \(P_{e}\), and \(P_{e_{sw}}\) as the failure probability of ground node \(u\), terrestrial link \(e\), and satellite link \(e_{sw}\) between satellite \(s\) and gateway \(w\), respectively. According to previous works [29; 32], the shortest path from \(u\) (or \(s\)) to \(c\) was selected by using the Dijkstra algorithm. Here, we divided the reliability of the path into two types: \(R_{uc}\) is defined as the reliability of the path from switch \(u\) to controller \(c\), and \(R_{uc}^{s}\) is defined as that from satellite \(s\) to controller \(c\), via gateway \(w\). Let \(\mathcal{U}_{u\to c}\) denote the node set and \(\mathcal{E}_{u\to c}\) denote the link set on the path from \(u\in\mathcal{U}\) to \(c\in\mathcal{C}\). \(R_{uc}\) can be calculated as:  
\[R_{uc}=\prod_{e\in\mathcal{E}_{u\to c}}(1-P_{e})\prod_{u\in\mathcal{U}_{u \to c}}(1-P_{u}), \tag{5}\]  
Similarly, \(R_{uc}^{s}\) is formulated as:  
\[R_{wc}^{s}=(1-P_{e_{sw}})\prod_{e\in\mathcal{E}_{s\to c}}(1-P_{e})\prod_{u \in\mathcal{U}_{e\to c}}(1-P_{u}). \tag{6}\]  
Hence, the average reliability of the integrated networks is defined as:  
\[R_{avg}=\frac{1}{n+k}(\sum_{u\in\mathcal{U}}R_{uc}+\sum_{s}R_{wc}^{s}),\quad c \in\mathcal{C},w\in\mathcal{W}, \tag{7}\]  
where \(n\) and \(k\) represent the number of switches and gateways, respectively, and \(\mathcal{C}\) represents a set of controllers.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods  
The experiments were in Python on a DELL computer from the UREN laboratory. This computer has an Intel(R) Core i7-10700 processor clocked at 2.90 GHz, 32 GB of RAM, and an NVIDIA Quadro P400 GPU. The models were configured in Python version 3.8.8, using the Keras API version 2.4.3 with TensorFlow version 2.3 as a backend.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Heart Rate Extraction
#### 2.4.6 Error Analysis of NIR-Based Measurements  
The methodology of NIR-based heart rate extraction differs from the RGB-based extraction. The NIR-based extraction uses a dedicated infrared light source, which means that the lighting conditions are largely controlled. However, unlike RGB-based extraction, patient motion can cause errors in the NIR-based measurements. Patient motion can add intensity variations to the skin pixels that are not caused by blood flow. If their frequency content is within the heart rate range, i.e., 0.66-3.6 Hz (40-220 BPM), the extraction of the heart rate during patient motion could become inaccurate. As the NIR camera is monochrome, the POS method cannot be applied to provide robustness against patient motion. Therefore, detection of patient motion is necessary for NIR-based heart rate extraction such that measurements distorted by patient motion can be discarded.  
To evaluate the effect of motion on the remote PPG extraction, we used the Lukas-Kanade optical flow [25] to quantify the motion in each frame of the NIR videos. We then found the mean optical flow magnitude of all pixels within the face ROI. Finally, we classified a segment of remote PPG signal to contain motion if any frame within it had a motion magnitude greater than some threshold. We then determined this threshold based on a tradeoff between the agreement and coverage of the NIR-based heart rate extraction.
## 5 Discussion
### How Is Performance Validated for Developed Techniques and Algorithmic Solutions?  
Validation approaches are essential for BN or GT methods, in order to analyze the performance of developed methodologies. In this SLR, it is observed that 56 out of 64 studies were validated by different mechanisms, and the remaining 8 studies have not reported the validation process, as shown in Figure 12. Sensitivity analyses (20% of included studies) perform a critical function in estimating the robustness of the outcomes on the principal analyses of the developed approaches. It is an important measure to evaluate the influence or impact of key hypotheses or variations on the specific infrastructure, including different analysis methods, protocol variations, outliers, definitions of results, and missing data, among others [48, 49, 50, 51, 52]. Another important aspect for validating the proposed technique is comparative analysis (20% of included studies), in which the outcomes of distinct models with different assumptions are compared with the developed approaches [79, 80]. The other validation mechanisms recognized in the included studies were expert evaluation, scenarios development, statistical analysis, empirical analysis, reachability graph, diagnostic analysis, checklists, cross-validation, and minimax analysis, 16%, 12%, 8%, 3%, 2%, 2%, 2%, 2%, 2%, and 2%, respectively.
## 2 Background
### Evaluation Metrics  
Most of the evaluation metrics of classification tasks are built on the confusion matrix concept that reveals the prediction results of the classification model for the test set. In the confusion matrix, the columns present the instances of the predicted classes, whereas the rows represent values for the actual classes, as shown in Table 1.  
This matrix indicates the ways our model is confused in predicting classes. TP presents the predicted positive instances whose actual classes are positive (i.e., no confusion). Similarly, TN shows the instances that are predicted as negative, which are also true. On the other hand, FP and FN show instances predicted as positive and negative, respectively, which are false. The next sub-sections present evaluation measures based on the confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline Actual Positive & True Positive (TP) & False Negative (FN) \\ Actual Negative & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
Accuracy can be defined as dividing the sum of correct predictions (TP and TN) to all instances, as given in the equation below.  
\[\text{Accuracy (A)}=\text{(TP+TN)}/\text{(TP+TN+FP+FN)} \tag{2}\]  
Precision shows the accuracy of the positive class. Precision is the division of TP to the total positive predictions (TP and FP), as shown in the following formula:  
\[\text{Precision (P)}=\text{(TP)}/\text{(TP+FP)} \tag{3}\]  
Recall (or sensitivity) shows the ratio of correctly detected positive classes, and it is defined as TP divided by the sum of positive classes (TP and FN), as shown below:  
\[\text{Recall (R)}=\text{(TP)}/\text{(TP+FN)} \tag{4}\]  
F-Measure (or F1 Score) compares models having different Precision and Recall values with a single evaluation measure. It can be defined as the harmonic mean of Precision and Recall, as shown below:  
\[\text{F1}=\text{2}\times\text{(P\times R)}/\text{(P+R)} \tag{5}\]
Three-dimensional cell culture techniques encompass a diverse range of methods, namely: 1. scaffold-based 3D cell culture systems; 2. hydrogel-based 3D cell culture systems; 3. spheroid formation tools; 4. microfluidic-based systems; 5. bioprinting technology; 6. an organic culture kit; 7. hanging drops and magnetic levitation systems; and 8. 3D cell culture media and reagents [22; 23]. These methodologies allow the cultivation of cells in structures resembling organs, tissues, or specific microenvironments. This offers researchers the ability to investigate intricate cellular processes and responses in a more contextually accurate manner. An ultralow attachment plate system is one of the spheroid formation tools [24]. These plates are used to promote spheroid formation by inhibiting cell attachment, and they have many advantages. The key characteristics and principles of ultraslow-attachment 3D cell cultures include reduced attachment, spheroid formation, and mimicking in vivo conditions. They can also be used in a wide range of applications. However, studies in the dental field are rare [25]. The purpose of this study was to grow PDLSCs using ultralow-attachment plates. Two types of PDLSCs originating from supernumerary and permanent teeth were used. We aimed to confirm the possibility of 3D cell culture in stem cell research in the dental field and to compare the characteristics of the two cell types in a 3D culture based on cell morphology and multilineage differentiation.
## 2 Materials and Methods
### Instrument and Data Collection  
The GPR multichannel system used in this study is Stream C, manufactured by IDS and available at the Department of Historical Studies of University of Torino. It has 34 antennas, 24 of which have the dipoles oriented vertically and 10 horizontally, with respect to the acquisition direction. A total of 23 radargrams with both the transmitter and the receiver antennas oriented vertically (\"VV\" with a horizontal separation of about 4 cm) and 9 horizontally (\"HH\" with a horizontal separation of about 10 cm) are, therefore, available for each acquisition profile (i.e., each swath). The central frequency of the antenna  
Figure 1: Multichannel GPR survey in an area within a residential insula in the archaeological park of Tindari: (**A**) map of the archaeological park with evidence of the surveyed area. (**B**) Zoomed-in view of the investigated area, with evidence from previous archaeological excavations in the area, superimposed to the GPR survey swaths (in white) and (**C**) position of the archaeological park in Sicily, Italy, highlighted by the white asterisk.  
is 600 MHz; however, frequency distributions centered at about 300 MHz were observed in the test site.  
The investigated area was covered with a total of 39 swaths acquired mainly in the SE to NW direction with a distance among the different acquisitions of about 1 m (Figure 1B) in order to cover the whole interested area with a dense distribution of traces. Few profiles in the perpendicular direction were also acquired around the central portion of the area where irremovable obstructions prevented a uniform profile acquisition. Data were acquired with an in-line acquisition step of 4 cm (same as the lateral separation of VV dipoles) and 512 samples per trace for an acquisition time of 80 ns. The data were acquired by moving the instrument on the survey areas thanks to an aluminum trolley on which the Stream C system was mounted. The data were georeferenced with a Geomax GNSS system in base-rover configuration and with RTK corrections, allowing for centimeter-level precision in localization.
## 4 Methodology
### CNN-Based Neural Network Module  
Given the grid-like feature matrix extracted from the contextual graphs, we further develop a convolution neural network-based backbone to learn representations of POIs. Taking \(\mathbf{M}_{p}^{L}\in\mathbb{R}^{(nl+1)\times d}\) as an example, we design a CNN based on 1D convolutional kernels to extract POI representations. This is because, unlike the feature matrices of images that exhibit spatial correlations, each dimension of POI features exists independently. Therefore, using 2D convolutional kernels to extract POI features from a grid feature matrix is not suitable. Specifically, we develop a neural network block that contains two convolution layers. The kernel sizes of these convolutional layers are \((nl+1,1)\) and \((1,1)\), respectively. The first convolutional layer is utilized to extract POI representations from the grid feature matrix, whereas the second layer serves for nonlinear feature transformation, a standard configuration in CNN architecture. Note that the choice of the kernel size is flexible. Any reasonable combinations of convolutional neural networks are available. Through such a neural network block, we can obtain the representation of POI \(p\) extracted from the geographical contextual graph:  
\[\mathbf{H}_{p}^{L}=\text{Conv2}(\text{Conv1}(\mathbf{M}_{p}^{L})) \tag{3}\]  
where \(\text{Conv1}(\cdot)\) and \(\text{Conv2}(\cdot)\) are predefined CNN layers. \(\mathbf{H}_{p}^{L}\) denotes the extracted representation of POI \(p\). Following the same operation, we can obtain the representation \(\mathbf{H}_{p}^{\text{T}}\) extracted from the temporal contextual graph.  
\(\mathbf{H}_{p}^{L}\) and \(\mathbf{H}_{p}^{T}\) represent the extracted representations from different contexts. However, in POI recommendation scenarios, the influence of various contextual factors may vary. To effectively integrate information from different contexts and derive the final POI representation, we have designed the following method:  
\[\mathbf{H}_{p}^{\text{F}}=\beta\cdot\mathbf{H}_{p}^{L}+(1-\beta)\cdot\mathbf{ H}_{p}^{T} \tag{4}\]  
\(\beta\in[0,1]\) represents the aggregation weight which controls the influence of different contextual factors on the final representation and \(\mathbf{H}_{p}^{\text{F}}\) represents the final representation of POI \(p\).
# Upper Second Molar Distalization with Clear Aligners: A Finite Element Study
## 4 Discussion
These assumptions may help do clarify the different results obtained in our study and in the one by [PERSON] et al. [7]. Furthermore, it should be remembered that registered efficiency in FEM simulations is limited to the insertion time and first reaction of the system to the aligner mismatch. Probably, in the clinical environment, in time, the force system will be modified, bringing to different results, as demonstrated by [PERSON] et al. in their 2019 study [16]. However, different activation techniques of aligners from our model still could not allow for time-dependent results. Simulations with time effect are solicited to clarify this point. Regarding contact pressure on the aligner, it seems that the effective pressure area for molar distalization is located on the meso-buccal surface of the second molar. Our results demonstrate that attachments on active units are actively involved in producing tooth movement, since in the ATT 3-7 configuration, the maximum stress area is located on the buccal surface of the attachment, differently from other configurations where the active area is located on gingival surfaces of aligners. Aligner fitting depends on the selected material and the thermoforming technique [17]. However, the aligners' gingival margin is known to be the most elastic section of the aligner and, consequently, the one with more chances to lose fitting [18]. Therefore, it could be postulated that active areas at the gingival edges of the aligners could reduce their efficiency within the clinical setting. Thus, it could be stated that ATT 3-7 and ATT 3-6 configurations present, in an in silico environment, almost equal effectiveness at distalizing the upper second molar. However, translating such evidence in a clinical setting, the ATT 3-7 configuration is indicated because of the distribution of active surfaces on the aligner and as a means of reducing the risk of a loose fitting of the aligner on the last molar, thanks to the higher elasticity of the aligner's terminal portion. However, if due to patient-related issues (anatomical factors, bad compliance, e.g., bonding of attachments on upper second molar results to be a very difficult procedure, ATT 3-6 may represent an efficient choice for upper distalization with clear aligners. It should always be kept in mind that this FEM analysis describes the force system during the insertion of the aligner and not the results of its elastic return.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 4 The Multi-Modal Minimal Scheduling Function
### TSCH Link Selection  
Supercells require a slightly modified TSCH link selection process. Presumably, the next active link (and hence how long a node can sleep) is determined when operations on the current link have concluded. At that point, a node iterates through all slotframes in ascending order of slotframe handles. For each supercell encountered, the node calculates how many slot offsets its first subcell is removed from the last subcell of the current supercell. At the same time, the node tracks a single supercell, which we call the Current Best Supercell (CBS). That is, the first supercell the node encounters automatically becomes the CBS. Then, for every next supercell, it compares the distance to the CBS with the distance to the given supercell. If the distance to the given supercell is less than or equal to the distance to the CBS and there is no overlap between them or the given supercell has precedence (see Section 2.1.2), the given supercell becomes the CBS. Finally, the node is left with the most appropriate next link to wake up for. Figure 8 puts the process of next active link selection in a handy flowchart.  
A backup link may also be selected when retrieving the next active link. Normally (i.e., without supercells) a backup link is selected from all Rx links that coincide with the ultimately selected next active link. Selection happens according to the lowest slotframe handle among all eligible backup links (since only Rx links are eligible). However, when using supercells, only Rx supercells that start at the same slot offset as the ultimately selected next active supercell, and fall entirely within said supercell, are eligible as a backup supercell.
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 5 Results
### Application to a Measured DRIR
#### 5.2.1 Incoherence Analysis and Mixing Time Estimation  
Once the PWD has been performed, the temporal evolution of the incoherence measure \(\Gamma(t)\) can be analyzed in order to estimate the mixing time, as proposed in Section 4.2. Figure 7 shows the result of this analysis; \(t_{\text{mix}}\) is estimated algorithmically by identifying the moment \(\Gamma(t)\) levels out at a near-maximal value after the initial period of instability due to the arrival of coherent early reflections [7].  
Figure 6: Comparison of broadband EDR analysis results for (**a**) the initial spectrum \(P_{0}\) and (**b**) the reverberation time \(T_{60}\), between “natural” 4 th-order PWD beamforming weights (red dashed lines), 4 th-order maximum front-to-back ratio beamforming (MFB, green dash-dot lines), and the synthesized cardioid distributions (black solid lines). Radii represent (**a**) dB and (**b**) seconds.  
Two notable features in the evolution of \(\Gamma(t)\) are its relatively high starting value (despite the fact that \(t=0\) should correspond to the arrival of the direct sound only) and its gradual increase throughout the reverberation tail (the maximum time displayed in Figure 7 is based on a preliminary broadband estimation of the noise floor time limit). The first can be explained in part by the presence of incoherent background noise for directions other than the direct sound DOA, as well as the time averaging necessary for the coherence calculation, which may introduce the influence of later time frames with higher overall incoherence. The second feature is due to the increasing overall presence of the highly incoherent noise floor, especially at higher frequencies where the reverberation time is usually very short.