esa-sceva/satcom-chunk-collection huggingface dataset:
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 4 Proposed Architecture
### Input and Feature Extraction  
The input \(\mathbf{X}^{(n)}\in\mathbb{R}^{1\times L\times M}\) is a three-dimensional (3D) array (consisting of the height, width, and channel dimensions) storing data segments of all sensor data from the \(n\)-th IMU, where \(M\) is the number of sensor data types per IMU and \(L\) is the number of data points in one segment. It can be expressed as \(\mathbf{X}^{(n)}=[\mathbf{x}_{1}^{(n)};\mathbf{x}_{2}^{(n)};\ldots;\mathbf{x }_{M}^{(n)}]\), where \(\mathbf{x}_{m}^{(n)}\) is a data segment of the \(m\)-th sensor from the \(n\)-th IMU and expressed in Equation (2). Note that \([(\cdot);(\cdot),\ldots,(\cdot)]\) denotes that the elements inside are arranged along the channel dimension. Each branch will apply a 1D CNN model to extract feature maps \(\mathbf{A}^{(n)}\in\mathbb{R}^{1\times W\times C}\), where \(W\) and \(C\) are the width and number of channels, respectively. The feature maps \(\mathbf{A}^{(n)}\) can be expressed as \(\mathbf{A}^{(n)}=[\mathbf{a}_{1}^{(n)};\mathbf{a}_{2}^{(n)};\ldots;\mathbf{a }_{C}^{(n)}]\), where the row vector \(\mathbf{a}_{c}^{(n)}=[a_{1,c}^{(n)},a_{2,c}^{(n)},\ldots,a_{W,c}^{(n)}]\) is a 1D feature map and \(a_{w,c}^{(n)}\) is a value at the \(w\)-th data point of the \(c\)-th channel. The following CNN models are considered as feature extractors due to their simplicity, low number of layers, and low computational complexities: LeNet5 [39], AlexNet [40], and VGG16 [41]. Note that these models originally consist of two-dimensional (2D) layers since they are  
Figure 1: Proposed MSE feature fusion architecture. It consists of the following stages: data inputs, feature extraction, merging-squeeze-excitation feature fusion, and classification. The inputs are the sensor data \(\mathbf{X}^{(n)}\) from \(N\) IMUs attached to several parts of the user body. Each branch extracts a set of feature maps \(\mathbf{A}^{(n)}\) independently by using a 1D CNN model. In the merging-squeeze-excitation feature fusion stage, each set of feature maps is calibrated by using a set of channel weights. A merging method combines the sets of channel-weighted feature maps \(\mathbf{P}^{(n)}\) and produces a new set of channel-weighted and merged feature maps \(\mathbf{Y}^{\textit{mise}}\), which is used later in the classification process.  
applied to image processing. Here, we implement their 1D versions by changing all 2D layers to be 1D layers. For example, 2D convolutional layers are replaced by 1D convolutional layers and 2D max pooling layers are replaced by 1D max pooling layers. The other parameters are unchanged such as numbers of filters and kernel sizes. These 1D CNN structures are summarized in Appendix A. The width \(W\) and the number of channels \(C\) of \(\mathbf{A}^{(n)}\) according to the considered CNN models are shown in Table 7. In addition to these three CNN models, other CNN models can be applied to extract \(\mathbf{A}^{(n)}\).
## 3 Methods  
### Measuring Sea Ice Motion and Deformation  
To measure sea ice motion and deformation, a processing chain including preprocessing of the satellite images after the appropriate band selection, measuring the sea ice motion using the MCC technique with hierarchical fashion, conducting a quality assessment on the sea ice motion measurement results, and an investigation on sea ice deformation were implemented (Figure 3).
## 5 Analysis of Factors Affecting Tunnel Deformation
### Time Series Analysis of Field Measurements  
Throughout construction, an extensive automated monitoring system was implemented to assess the effects of the foundation pit construction on the tunnel. The construction timeline was divided into four stages: 1 earthwork excavation; 2 support removal, interspersed with the construction of the negative three-layer middle plate of the foundation pit; 3 encompasses various layer floors and middle plate construction; 4 completion of the North Hub project, alongside construction activities in the adjacent area. Figure 10 illustrates these key construction steps.  
Figure 9: Vertical displacement distribution of ground surface.  
The previous analysis emphasizes notable displacement near the center of the adjacent foundation pit. Accordingly, Figure 11 shows the horizontal and vertical displacements at Monitoring Point 1, which is located in the tunnel section at the pit's center. Initial disturbances from constructing the diaphragm wall resulted in significant tunnel deformation. At the start of excavation, the tunnel experienced a horizontal displacement of 13.8 mm and a vertical displacement of \(-5\) mm. In Stage 1, both horizontal and vertical displacements progressively increased with excavation, demonstrating a linear relationship with the excavation process. During Stage 2, while constructing the negative three-layer middle plate, tunnel displacements remained relatively stable. However, displacements increased further during support removal, particularly during the removal of the fifth support, when the vertical displacement rate surged significantly due to the decreased rigidity of the concrete support after removal. In Stage 3, the vertical displacement of the tunnel remained stable, while there was a slight increase in horizontal displacement. Once the floor construction was completed, the foundation pit stabilized. In Stage 4, tunnel displacement initially exhibited a slow upward trend, which then decreased until stabilization. The excavation of the foundation pit was the primary factor causing displacement in the adjacent tunnel. However, the impact of support removal, particularly the removal of the high-rigidity concrete support, was significant. After completing the negative one-layer middle plate construction, the foundation pit and tunnel displacement stabilized.  
Figure 10: Key construction steps.  
Figure 11 shows that both horizontal and vertical displacements of the tunnel exceed the deformation monitoring control limit (10 mm), triggering several red alert notifications. This indicates that, under the current support conditions, the displacement of the adjacent tunnel remains uncontrollable. The next section aims to refine the parameters of the retaining structure in order to better control tunnel displacement.
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 7 Greedy Online Scheme  
Offline setup lacks flexibility adapting to changing traffic conditions or to different network conditions (e.g., new readers). An alternative to this design is allowing each reader to decide _autonomously_ and _online_ which is the traffic priority that it must establish. The network designer decides the utility limits \((u_{\max},u_{\min})\) for each reader (usually according to the traffic class, as in the previous sections). Then, during operation, each reader checks if the achieved utility is within the target range and corrects its operation.  
The algorithm works as follows:  
* Each reader operates independently, and for every decision period it estimates its interrogation cycle \(\phi=\text{Cycles}\) in the last decision period/Decision period duration.
* The utility \(u\) is computed from \(\phi\) as \(\text{sigma}(a\phi-b)\), \(a\) and \(b\) being the parameters associated to the particular reader traffic (see Table 2).
* If \(u>u_{\max}\), then \(\Delta\leftarrow\Delta+5\).
* If \(u<u_{\min}\), then \(\Delta\leftarrow\Delta-5\).
* Otherwise, \(\Delta\) is kept fixed.  
This algorithm is dubbed as _greedy_ since it increments its class unconditionally when the performance is degraded.  
We have tested this scheme in the hospital environment studied in the previous section, for a decision-period-duration of 120 s, setting different utility limits (but homogeneous for all readers). Figure 10 shows the evolution of the total utility given by Equation (1) for a simulation trace. As shown, the utility limits determine the long-term performance and the stabilization period. The best performance is achieved for a high (close to 1) \(u_{\max}\). For lower values, the long-term utility tends to drop from a maximum and then stabilize there. In these cases, the transient period is about 4 h (about 120 decision periods).  
Figure 9: Configuration utility as a function of \(\Delta\) setups.  
Further insight in the online scheme can be seen in Figures 11 and 12. They show respectively the utility and \(\Delta\) traces for each reader in the hospital setup. As shown, the patient monitoring readers gain utility at the expense of the other groups. The convergence for these high-priority readers is quick (\(\sim\)15 decision periods), due to the greedy nature of the algorithm. Besides, the medium-priority readers (door readers) steadily increase their \(\Delta\) but at a slower pace than the low-priority ones (storage inventorying).  
Figure 10: Total utility with the varying min and max decision thresholds.  
Figure 11: Utility traces. (**a**) High priority readers for patient monitoring. (**b**) Medium priority readers of entrances. (**c**) Low priority readers for storage monitoring.
## 3 Results and Discussion
### Effect of Impeller Speed Evaluated from PEPT Experiments
For all the cases, in the location beneath the bottom arms (\(0<Z<20\) mm) the velocity vectors were remarkably short and not significantly influenced by the increase of the impeller speed. Hence, according to the velocity maps the beads at the bottom of the attritor seemed to be trapped in a dead region of the mill. These observations imply that the transfer of energy between the impeller and the bottom beads was still ineffective and/or impeded by the boundary effect of the vessel base. This was better highlighted in Figure 6 by the calculation of the normalised tracer occupancy, defined as the cumulative time spent by the tracer particle in a cell of the grid divided by the total time of the experiment. This quantity can be considered as an indicator of the tracer accessibility within the different zones of the equipment. At 300 rpm and 450 rpm the normalised occupancy was quite uniform, suggesting a similar recirculation of the beads within the vessel and the absence of preferential paths. Both the upper part of the media bed and the bottom corners of the grinding chamber showed low values of occupancy because the tracer in proportion spent less time in these zones compared to the rest of the pot. On the other hand, at 600 rpm the normalised occupancy map revealed the presence of a region of high occupancy at the bottom of the vessel where the tracer was trapped for long time. The possible explanation for the area of high occupancy is that at 600 rpm the lower recirculation loop obstructed the re-admission of the tracer in the upper part of the bed. The difficult re-admission of the tracer could also explain the longer relative time spent by the tracer at the mill bottom compared to the rest of the pot.
## 4 Experimental Simulations and Results  
In this section, a simulation analysis is performed on the operational task of the Huoqiu-Caomiao section of the Fuyang-Lu'an Railway in China, which includes nine target points spanning a total distance of 19.188 km. The characteristics of the line are illustrated in Figure 4, including gradient, curve radius, and speed limit. The vehicle data used for this study were sourced from the JW-4G railway catenary maintenance vehicle, and its corresponding specifications are presented in Table 1.  
Figure 4: Characteristics of the operational line: (**a**) gradient, (**b**) curve radius, and (**c**) speed limit.
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
###### Abstract  
The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale non-dedicated sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively.  
mobile crowd-sensing; behaviometrics; energy efficiency; user profiling; smart cities +
Footnote †: journal: Sensors
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 3 Dataset and Our Method  
### Dataset  
The HRSID [19; 20] is a repository for ship detection in high-resolution SAR images. It contains over 16,951 instances of ships, as well as 5604 high-resolution SAR photos. The HRSID was created using the COCO datasets, which include images with varying marine areas, resolutions, water conditions, and coastal ports. These allow researchers to compare their methods against those of other researchers. Figure 1a,b show the images from HRSID repository. The three resolutions of the HRSID's high-resolution SAR images are: 1 m, 3 m, and 0.5 m. Table 1 displays some detailed information of the images from HRSID dataset.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Medical Prediction
#### 5.3.1 Static Medical Prediction  
A variety of prediction tasks are included in static medical prediction. One of the simplest categories is the prediction of some outcome without considering time constraints. For example, in heart failure prediction studies, early methods mainly used KNN [88], Logistic Regression (LR) [89; 90], SVM [91] and MLP [92]. [PERSON] et al. [70] introduced the conversion of data into vectors based on conventional methods and used distributed representation methods with several ANN and linear models for prediction.  
In addition, [PERSON] et al. [71] proposed an enRBM model to perform prediction tasks. This model is capable of fast characterization learning for large data with high dimensionality compared to previous models and can support various medical analysis tasks. For example, they plugged in a logistic regression classifier after the model to perform suicide risk stratification, and in further experiments demonstrated that the results obtained using the full EHR data were superior to those using only diagnostic codes. In addition, [PERSON] et al. [1] proposed Deep Patient using AE-generated data vectors to access an LR classifier for various ICD-based disease diagnoses and obtained high k-values for the precision@k metric.  
In addition to the data prediction performed using the above, deep learning has been able to use medical texts in EHR for various types of prediction due to the rich medical information contained in EHR. Medical texts are classified into structured texts and unstructured texts in the same way as conventional texts. [PERSON] et al. [72] proposed a general multi-task framework for disease onset prediction based on previous studies [93; 94; 95]. This framework does not require special feature engineering and can utilize both structured and unstructured data compared to traditional methods.
## 2 Materials and Methods
### Near Real-Time Combined Processing  
_(a) Combination of Waterline Height Threshold Maps_  
At this stage the interpolated waterline height maps from the SAR and the model are combined to form a single waterline height map. A possible approach to this would be to assimilate a sequence of SAR WLO maps into the model WLO map as it evolves over time in order to improve the latter, perhaps using a sequential ensemble Kalman filter [13; 14]. While this may be the optimum solution, in this case there is a difficulty in updating the model WLO map with height innovations because the simulation library approach used in the modelling means that, at this stage, there is no hydrodynamic model being run into which to assimilate the innovations.  
The simpler approach adopted here is to use the best linear unbiased estimate [59]. Consider the simple weighting scheme  
\[O(x,y,t)=(w_{1}O_{SAR}(x,y,t)+w_{2}O_{MOD}(x,y,t))/(w_{1}+w_{2}) \tag{1}\]  
where \(O_{SAR}(x,y,t)\) and \(O_{MOD}(x,y,t)\) are SAR and model water-level observations at position (\(x,y\)) and time \(t\), \(w_{1}\) and \(w_{2}\) are weights, and \(O(x,y,t)\) is the combined estimate. Suppose \(O_{SAR}(x,y,t)\) and \(O_{MOD}(x,y,t)\) are independent unbiased estimators with variances \(\sigma_{1}{}^{2}\) and \(\sigma_{2}{}^{2}\), respectively. Then, if \(w_{1}=1/\ \sigma_{1}{}^{2}\) and \(w_{2}=1/\ \sigma_{2}{}^{2}\), \(O(x,y,t)\) is the linear combined unbiased estimate with the minimum variance. The variance of O(\(x,y,t\)) is  
\[\sigma_{1}{}^{2}\sigma_{2}{}^{2}/(\sigma_{1}{}^{2}+\sigma_{2}{}^{2}) \tag{2}\]  
The combined variance is lower than either \(\sigma_{1}{}^{2}\) or \(\sigma_{2}{}^{2}\), resulting in an improvement in accuracy, though this result holds only if the variances are known.  
Rural SAR WLOs may well be more accurate than model WLOs at SAR acquisition time. But as time passes, the SAR flood extent will become more out-of-date until the next SAR image is acquired, whereas the model will be updated every 3 h, so the relative accuracies of SAR and model data will change with time. A simple way of taking this into account is by using an exponential \"forgetting factor\" and setting  
\[w_{1}=exp(-(t-t_{0})/\tau)/\sigma_{1}{}^{2} \tag{3}\]  
where \(t_{0}\) is the time of the last SAR acquisition, and \(\tau\) is the nominal lifetime of this SAR image's usefulness. As an example, assuming \(\sigma_{1}=0.3\) m, \(\sigma_{2}=0.4\) m and \(\tau=2\) days, then at SAR image acquisition time (\(t=t_{0}\)) around flood peak, \(w_{1}/(w_{1}+w_{2})=0.64\) and \(w_{2}/(w_{1}+w_{2})=0.36\), whereas at \(t=4\) days, \(w_{1}/(w_{1}+w_{2})=0.2\) and \(w_{2}/(w_{1}+w_{2})=0.8\).  
_(b) Flood Detection in Urban Areas_  
The final stage is to extract urban water regions from the effective height image _eff_th(x,y)_ produced from the model return-period maps, using the combined SAR and model WLO threshold map \(O(x,y,t)\). For pixels in urban areas, if  
\[\textit{eff\_th(x,y)}<O(x,y,t) \tag{4}\]  
then the pixel is flooded, else not flooded.
## 2 Materials and Methods  
This retrospective study was reviewed and approved by the University of Ferrara Postgraduate School of Orthodontics Ethics Committee and assigned number 2/25. All patients treated with clear aligners (CAs) from January 2024 to October 2024 at a private orthodontics clinic in Varese (Italy) were retrospectively selected, and provided informed consent. The following inclusion criteria were applied: (1) presence of all teeth up to the third molars; (2) availability of all initial diagnostic records (intra- and extra-oral photos, panoramic x-ray, lateral cephalogram, and conventional digital intra-oral models); and (3) conventional digital model files from intra-oral scan (C.stl) and intra-oral models AI-generated (ALstl) by Dental Monitoring software (Dental Monitoring, Paris, France) at the same timepoint during treatment. After the application of these inclusion criteria, the sample comprised 38 patients and a total of 152 models. Of these, 38 upper and 38 lower jaw models were obtained via conventional intra-oral scanner Itero Element(tm) 5D (Align Technology, San Jose, CA, USA), and 38 upper and 38 lower models via Dental Monitoring.  
Conventional intra-oral scans were taken by the same clinician according to the manufacturer's instructions. As such, the scan started from the occlusal area of lower teeth, from left to right, continuing lingually, with an occluso-lingual movement from lower right to the lower left molars. Scanning then proceeded buccally, with an occluso-buccal movement from the lower left to the lower right molars. Finally, if no scanning defects were observed, the same scanning procedure was applied to the upper arch and for bite registration. If scanning defects were detected, the operator repeated the scan of the area. The scans were then post-processed and downloaded in.stl format.  
For Dental Monitoring(r) (DM, Dental Monitoring, Paris, France) scans, patients were instructed to take photographs with their smartphone according to the manufacturer's instructions and following the application's workflow. The AI.stl files were subsequently downloaded from the Dental Monitoring Dashboard.  
Cast analysis consisted of comparing the transverse measurements in millimeters taken on both upper and lower arch models generated via C.stl and AI.stl files obtained during the same check-up appointment (Table 1). All measurements were made by the same orthodontist, using the software \"3 Shape Ortho Viewer 2020\" (3 Shape, Copenhagen, Denmark) as shown in Figures 1 and 2. Measurements were subsequently reviewed by another orthodontist, an expert in the use of such software. Ten percent of measurements were then repeated by the initial operator 30 days after the first assessment to test intra-examiner reliability.
## 2 Problem Setting  
The definitions used in this work are presented below.  
* **Domain**: A domain \(D\) consists of the following two components: a feature space, \(\chi\), and a marginal probability distribution, \(P(X)\). \(\chi\) is the space that includes all the term vectors, and \(X\) is an individual learning sample. In general, different domains have different feature spaces or different marginal probability distributions.
* **Source domain**: \(D_{S}=\{(X_{S_{i}},Y_{S_{i}})\}_{i=1}^{n_{S}}\) refers to a set of labeled reviews from a certain domain. \(X_{S_{i}}\) is the \(i\)-th labeled review, denoting one product review in the source domain. \(Y_{S_{i}}\) is the sentiment label of \(X_{S_{i}},Y_{S_{i}}\in\{+1,-1\}\), where the sentiment labels +1 and -1 denote positive and negative sentiments, respectively. \(n_{S}\) is the number of labeled instances in the source domain and denotes the total number of product reviews in the source domain.  
* **Target domain**: \(D_{T}=\{(X_{T_{i}})\}_{i=1}^{n_{T}}\) refers to a set of unlabeled reviews from a domain different from but related to the source domain. Here, \(X_{T_{i}}\) is the \(i\)-th unlabeled review corresponding to one product review in the target domain, and \(n_{T}\) is the number of unlabeled reviews in the target domain.
* **Cross-domain sentiment classification**: Cross-domain sentiment classification is defined as the task of training a binary classifier using labeled \(D_{S}\) to predict the sentiment label \(Y_{T_{i}}\) of a review \(X_{T_{i}}\) in the target domain.