esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
###### Abstract  
Radio-frequency (RF) tomographic imaging is a promising technique for inferring multi-dimensional physical space by processing RF signals traversed across a region of interest. Tensor-based approaches for tomographic imaging are superior at detecting the objects within higher dimensional spaces. The recently-proposed tensor sensing approach based on the transform tensor model achieves a lower error rate and faster speed than the previous tensor-based compress sensing approach. However, the running time of the tensor sensing approach increases exponentially with the dimension of tensors, thus not being very practical for big tensors. In this paper, we address this problem by exploiting massively-parallel GPUs. We design, implement, and optimize the tensor sensing approach on an NVIDIA Tesla GPU and evaluate the performance in terms of the running time and recovery error rate. Experimental results show that our GPU tensor sensing is as accurate as the CPU counterpart with an average of 44.79\(\times\) and up to 84.70\(\times\) speedups for varying-sized synthetic tensor data. For IKEA Model 3D model data of a smaller size, our GPU algorithm achieved 15.374\(\times\) speedup over the CPU tensor sensing. We further encapsulate the GPU algorithm into an open-source library, called cuTensorSensing (CUDA Tensor Sensing), which can be used for efficient RF tomographic imaging.  
radio frequency; tomographic imaging; tensor; GPU +
Footnote †: journal:
# Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
## 4 Experimental Setup
### Training Environment
#### 4.3.1 State Space  
The representation of the states of the environment is one of the most critical factors that can impact the learning capability and performance of the agents. Typically, the state is formalized through a vector of a certain dimensionality, which should provide enough information to facilitate learning. Nevertheless, representations with higher dimensionality will suffer from a higher computational effort to train an effective model.  
Therefore, in this work we take the state representation proposed by [PERSON] et al. [25], where the state is formalized through the agents' position and speed information. More specifically the state \(s_{i}\) of an agent is the vector \(s_{i}=[lat,lon,hdg,spd]\), i.e., latitude, longitude, heading, and speed. These values are normalized into the range \([0,1]\) to make it easier for the model to be trained.
## 3 Theoretical Background
### Particle Swarm Optimization (PSO)  
Particle swarm optimization is a meta-heuristic searching algorithm that is used to obtain the optimal solution to numerical problems. PSO was originally proposed by [PERSON]  
Figure 5: Overview of preprocessing steps used in VAD [24].  
and Elberhartin in 1995, [57] influenced by the social behavior of bird flocking or fish schooling. Currently, the algorithm has been well-tested on many complex mathematical problems and found to be efficient and fast. The pseudocode for PSO is presented in Algorithm 1. PSO initially populates particles (points in the solution space), and these particles are considered a probable solution to the optimization problem, which is given by a fitness function. In every iteration, a new position for the particles is calculated using several parameters such as the velocity vector, global best solution, local best solution, etc.  
Figure 6 shows the velocity and position update procedure for \(i^{th}\) particle at time step \(t\) using Equations (7) and (8), respectively. \(p^{i}_{best}\) represents the particle's information gained from its past movements. Similarly, \(g_{best}\) represents the collective information gained by all the particles.  
The velocity update of each particle is performed using the following equation:  
\[v_{i}(t+1)=w\cdot v_{i}(t)+c_{1}\cdot\Big{(}p^{i}_{best}-x_{i}(t)\Big{)}+c_{2} \cdot(g_{best}-x_{i}(t)) \tag{7}\]  
where \(c_{1}\), and \(c_{2}\) are priority factors used to configure the dominance of \(p^{i}_{best}\) and \(g_{best}\). The new position \(x_{i}(t+1)\) of the \(i^{th}\) particle is calculated as:  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{8}\]  
In Equation (7), the first term (\(w\cdot v_{i}(t)\)) contains the variable \(w\), which represents the inertia weight. Therefore, the whole term is known as the inertia component, which is responsible for maintaining the particle moment it initially possessed.  
The value of \(w\) is usually maintained between 0.8 and 1.2. Setting up a smaller value for \(w\) reduces the algorithm's search space and converges quickly to the optima (sometimes trapping in local optima), while higher values force the algorithm to search over a wider space (which sometimes skips the global optima).  
The second term in Equation (7), \(c_{1}\cdot\Big{(}p^{i}_{best}-x_{i}(t)\Big{)}\), serves as the particle's memory and is known as cognitive component. It forces the process to search in the area where the best solution can be found based on the particle's perspective. This coefficient (\(c_{1}\)) is usually set to 2.  
Figure 6: Particle velocity update procedure in PSO.  
The third term in Equation (7), \(c_{2}\cdot(g_{best}-x_{i}(t))\), is known as the social component. It forces the particle movements towards the area belonging to the best of each particle. This component is also set to 2.
## 3 Discussion  
We confirm results reported by [PERSON] and colleagues [10]: a second AACT is an effective treatment option for POEMS patients relapsing after a first AACT. A second exposure to high-dose melphalan seems to be well-tolerated and produces a good response, without more neurotoxic drugs, in patients with significant neurological impairment [11]. We have observed some differences between our cases and other published papers.  
The [PERSON] study [10] described relapses within one year from a first AACT; in our cases relapses were late, and in keeping with previous studies [6; 12; 13]. The median interval from the first AACT to the second was 40 months (range, 21-125 months) in [PERSON] [10] and 91 months in ours.  
In previous studies the incidence of complications associated with AACT in patients with POEMS syndrome was found to be associated with age and comorbidities [13; 14; 15]; therefore, a correct re-evaluation of relapsed patients in terms of fitness for AACT needs to be done. The median age at second AACT was comparable in [PERSON]'s study [10] and in our two patients, as well as our data in terms of low rates of toxicity. We decided to perform a second AACT in patients with an aggressive, but melphalan chemosensitive, POEMS. We also agree with the findings of aberrant cytokines, which could correlate with the risk of peri-transplant complications; if reduced with a good control of the disease progression due to an optimal induction therapy, it could explain the low rates of complications and toxicities in these studies.  
Both our patients required a second PBSC collection, pointing out the issue of a reduced stem cells pool, and one patient required the combination of G-CSF and plerixafor [16; 17].  
Patient 1 was previously treated with lenalidomide, which is generally considered to hamper PBSC collection, so treatment with lenalidomide does not seem to reduce the possibility of mobilization. However, in young and fit patients affected by an aggressive POEMS syndrome we are considering harvesting a greater number of PBSCs up front, and cryopreserving a suitable CD34+ cell dose for a second AACT, to be used in the case of a relapse. In addition, the Rd combination produced a deep response in both patients and it can be therefore used as a purging therapy or as a bridge to transplant, in late relapses after a first AACT.  
From our experience, we are considering harvesting a greater number of stem cells up front, to be used in the case of a relapse in patients affected by an aggressive POEMS syndrome. Following the success of our two patients, we modified our clinical practise starting with the stem cells harvest procedure.
## 2 Optical Field of DD-MZM Output
### DD-MZM with Carrier Arm (CA)  
The effectiveness of OSSB modulation to overcome DPF diminishes with increasing \(m\); when \(m\) increases, the carrier power of the DD-MZM output optical field decreases, and more sideband orders are formed. To compensate for the decreased carrier power due to increasing \(m\), the DD-MZM series is added with a carrier arm (CA), as portrayed in Figure 1. The continuous optical wave signal generated by LD is shared using an optical splitter (OS); one part is used as an optical input of DD-MZM, and the other part is forwarded to CA to be recombined with DD-MZM output using an optical combiner (OC). The optical signal on DD-MZM is modulated by the RF signal with a similar configuration to DD-MZM with a detailed configuration, as shown in Figure 1.  
The comparison of LD power entered into DD-MZM and the total LD power is expressed as  
\[r=\frac{P_{DD-MZM}}{P_{in}}=\frac{P_{in}-P_{CA}}{P_{in}} \tag{16}\]  
where \(P_{in}\), \(P_{DD-MZM}\), and \(P_{CA}\) are the power sharing ratio, LD output power, and power input into DD-MZM and power input into CA, respectively. Thus, the DD-MZM output optical field (\(E_{DD-MZM}(t)\)) can be expressed as  
\[E_{DD-MZM}(t)=\frac{r}{2}\sqrt{2P_{in}}\Bigg{\{}\sum\limits_{n=-\infty}^{ \infty}j^{n}.J_{n}(m).\left(e^{j\pi\gamma}+e^{jn\theta}\right)e^{j2\pi(f_{c}+ nf_{m})t}\Bigg{\}} \tag{17}\]  
We assume that the optic arm length is less than 1 m. Therefore, the phase of \(P_{CA}\) does not change significantly. The CA optical field (\(E_{CA}(t)\)) can be expressed as  
\[E_{CA}(t)=(1-r)\sqrt{2P_{in}}e^{j2\pi f_{c}t} \tag{18}\]  
The optical field output of OC (\(E_{TX}(t)\)) is the sum of \(E_{DD-MZM}(t)\) with \(E_{CA}(t)\), given by  
\[\begin{array}{l}E_{TX}(t)=E_{DD-MZM}(t)+E_{CA}(t)\\ =\sqrt{2P_{in}}\Bigg{\{}(1-r)e^{j2\pi f_{c}t}+\frac{r}{2}\sum\limits_{n=-\infty }^{\infty}\left(e^{jn\gamma}+e^{jn\theta}\right).j^{n}.J_{n}(m).e^{j2\pi(f_{c} +nf_{m})t}\Bigg{\}}\end{array} \tag{19}\]  
From Equation (19), it can be seen that the optical signal spectrum of the DD-MZM output with CA comprises an optical carrier with infinite sideband order. Various forms of modulation schemes can still be generated by setting the parameter \(\theta\) and \(\gamma\). The magnitude of carrier power compensation of the DD-MZM output optical field that decreases because the increasing \(m\) can be adjusted by regulating the value of \(r\). The optical field of the DD-MZM output with CA will always have a carrier even if \(J_{0}(m)\) is zero.
## 4 Experimental Results
### When the ROV Is Facing Directly Toward the Current  
In this scenario, the ROV was oriented directly against the direction of the ocean current, resulting in minimal disturbance from wake flow. The simulation was configured with consistent parameters for the first and second path segments. Specifically, the ROV started at position [0, 0, \(-\)40] and aimed to reach the target location [\(-\)15, \(-\)15, \(-\)60]. The yaw angle at the target was set to \(0^{\circ}\), while the ROV's initial yaw was \(165^{\circ}\), causing it to face nearly opposite the current.  
\(\beta_{\rm c}=\pi/4\), representing the current's direction in the horizontal \(xy\)-plane, was measured counterclockwise from the x-axis, and  
\(\alpha_{\rm c}\approx 0.955\) rad, representing the elevation angle of the current from the \(xy\)-plane toward the z-axis.  
This configuration ensured that the current vector was oriented near the target direction, resulting in an angular separation of approximately \(180^{\circ}\) between the current and the desired trajectory. The resulting ROV trajectory under these conditions is illustrated [25] in Figure 9.  
In this sample, the ROV started at [0, 0, \(-\)40] and finally reached [\(-\)14.8114, \(-\)14.5905, \(-\)60.039]. Using the Euclidean distance formula, there was approximately 0.4525 m between the final position and the target. The component-wise deviations were 0.1886 m in the \(x\)-direction, 0.4095 m in the \(y\)-direction, and 0.0390 m in the \(z\)-direction.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 3 Energy Drain Attacks in WSNs
### Motivation And Methodology  
It is very obvious that energy attacks damage networks by quickly draining the node's battery power. In this paper, we review the state-of-the-art research in this domain. To achieve this, we follow the following process:  
* Step 1: Search the literature for \"energy drain attack WSNs\";
* Step 2: Extract lists from Step 1 by related titles as well as related abstracts;
* Step 3: Conduct a detailed analysis of the research content.  
To organize a comprehensive review, we selected the IEEE, ACM, Scopus, and Google scholar databases. Papers were examined with the following keywords: \"energy drain attack WSNs\", \"EDA WSNs\", \"energy attack WSNs\", \"battery attack WSNs\", and \"energy security and attack-type WSNs\". We searched pure WSNs by excluding the \"IoT\" and selected only highly credible papers published in good venues. We limited the period of publication to between 2019 and 2024. After this process, we chose sixty-five papers in the research areas. Figure 3 illustrates the number of articles from each year, while Figure 4 shows the aspect of layering. As illustrated in these figures, research papers in the network layer published in 2021 are the most valuable sources for this paper.
# Foot Insole Pressure Distribution during the Golf Swing in Professionals and Amateur Players
###### Abstract  
There are numerous articles that study the ground reaction forces during the golf swing, among which only a few analyze the pressure pattern distributed on the entire surface of the foot. The current study compares the pressure patterns on the foot insoles of fifty-five golfers, from three different performance levels, playing swings with driver and 5-iron clubs in the driving range. Five swings were selected for each club. During each swing, ultra-thin insole sensors (4 sensors/cm\({}^{2}\)) measure foot pressure at the frequency of 100 Hz. To perform statistical analysis, insole sensors are clustered to form seven areas, with the normalized pressure of each area being our dependent variable. A video camera was used to label the five key instants of the swing. Statistical analysis demonstrates a significant difference between the pressure distribution pattern of the left and right feet for both driver and 5-iron. However, the pressure distribution pattern remains almost the same when switching the club type from 5-iron to driver. We have also observed that there are significant differences between the pattern of professionals and players with medium and high handicap. The obtained pattern agrees with the principle of weight transfer with a different behavior between the medial and lateral areas of the foot.  
golf swing; biomechanics; pressure pattern; weight transfer; sensors +
Footnote †: journal: Applied Sciences
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results  
### Selection of the Data, Variables and Methods  
The capability of the different features in damage area recognition was first studied (see Section 3.1). Other variables were some traditional stand level forest characteristics (Table 1) as well as slope, aspect and altitude calculated from the digital elevation model. The stand level variables tested were mean diameter, mean height, age, basal area and the volume of growing stock as well as volumes by tree species groups (Table 1).  
The methods studied in windstorm damage recognition were the improved k-NN with feature optimization based on a genetic algorithm, called ik-NN method, multinomial logistic regression (MLR) and support vector machine (SVM) (Sections 3.3-3.5). The three models were used to classify the training data into the three categories, non-damaged, severely damaged and slightly damaged. The accuracies were validated using the validation data. The explanatory variables in the models were the SAR metrics calculated from the SAR images before and after the damage as well as the other characteristics mentioned above.  
Each method has several parameters to control the performance of the method. Furthermore, the combinations of the explanatory variables is large. The number of the optional methods and variable combinations is thus really large. It was not possible to carry out all possible experiments. Only the results from a few tested combinations are reported here.
_Article_  
Virtual Grounding Point Concept for Detecting Abnormal and Normal Events in Home Care Monitoring Systems  
[PERSON] 1,*(r), [PERSON] 2(r) and [PERSON] 3  
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan; [EMAIL_ADDRESS] Graduate School of Engineering, Osaka City University, Osaka 558-8585, Japan; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
Received: 31 March 2020; Accepted: 22 April 2020; Published: 25 April 2020  
**Featured Application: This research can be applied to home care monitoring systems to assist in providing care for dependent persons by analyzing abnormal events or falls.**  
**Abstract:** In this paper, an innovative home care video monitoring system for detecting abnormal and normal events is proposed by introducing a virtual grounding point (_VGP_) concept. To be specific, the proposed system is composed of four main image processing components: (1) visual object detection, (2) feature extraction, (3) abnormal and normal event analysis, and (4) the decision-making process. In the object detection component, background subtraction is first achieved using a specific mixture of Gaussians (_MoG_) to model the foreground in the form of a low-rank matrix factorization. Then, a theory of graph cut is applied to refine the foreground. In the feature extraction component, the position and posture of the detected person is estimated by using a combination of the virtual grounding point, along with its related centroid, area, and aspect ratios. In analyzing the abnormal and normal events, the moving averages (_MA_) for the extracted features are calculated. After that, a new curve analysis is computed, specifically using the modified difference (_MD_). The local maximum (_lmax_), local minimum (_lmin_), and half width value (_vhw_) are determined on the observed curve of the modified difference. In the decision-making component, the support vector machine (_SVM_) method is applied to detect abnormal and normal events. In addition, a new concept called period detection (_PD_) is proposed to robustly detect the abnormal events. The experimental results were obtained using the Le2i fall detection dataset to confirm the reliability of the proposed method, and that it achieved a high detection rate.  
**Keywords:** virtual grounding point; abnormal and normal events; mixture of Gaussians; moving average; modified difference; half width value; support vector machine
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 3 Opportunities  
In this section, we explore the opportunities that AI applications present to the smart healthcare field.  
(1) AI plays a pivotal role in predictive healthcare modeling by processing vast amounts of data, including EHRs, medical imaging scans, and genetic information. Using advanced ML techniques such as DL and NLP, AI algorithms can identify complex patterns and subtle anomalies that may indicate potential health issues. They can also uncover hidden correlations and nonlinear relationships across diverse data sources, enabling more accurate predictions and personalized insights [33]. This capability enables dynamic prediction of various outcomes, such as disease progression and patient readmission rates, thereby supporting informed decision making for healthcare professionals. By continually learning from new data, AI-driven predictive modeling provides personalized insights  
Figure 1: The overview workflow of artificial intelligent applications in the smart healthcare field.  
and treatment recommendations, ultimately enhancing patient outcomes and operational efficiency within healthcare organizations [34].  
(2) AI facilitates knowledge discovery from both structured and unstructured healthcare data [35]. For example, ML and DL algorithms can extract structured information from unstructured data such as images, audio, and videos containing medical information. NLP algorithms can derive valuable insights from clinical notes, research articles, and patient feedback. Additionally, these AI algorithms can glean useful information from structured data such as EHRs. By analyzing these diverse data, AI systems gain a comprehensive perspective, expand the boundaries of knowledge, and adapt to new scenarios and challenges, enabling adaptive learning and continuous improvement [36]. For example, AI-powered diagnostic tools can learn from previous cases to enhance their accuracy over time, thereby improving diagnostic outcomes and treatment recommendations.  
(3) AI provides cross-domain insights that promote research and innovation in healthcare. For example, AI-powered data analytics platforms can integrate data from various sources, such as patient records, genetic data, and environmental factors, to identify correlations and trends that may not be apparent to human analysts. This interdisciplinary approach enables researchers to gain deeper insights into disease mechanisms, treatment effectiveness, and public health trends. Moreover, AI-driven tools streamline workflows and enhance resource utilization, enabling healthcare organizations to allocate resources more efficiently and deliver better care to patients [37]. Overall, AI holds immense promise for transforming the smart healthcare landscape by unlocking valuable insights, improving decision making, and driving innovation across the healthcare ecosystem.
## 4 Applying Convolutional Neural Network (CNN) to Gene Expression Data in Formed Clusters
#### 5.2.1 Results of Patient State Diagnosis Based on the Use of the Random Forest Algorithm  
Figure 8 presents the simulation results using the random forest algorithm applied to gene expression data from different cluster structures.  
As mentioned above, the classification results were evaluated on test datasets. Analysis of the results allowed concluding that, based on the classification quality criteria, the four- and six-cluster structures are more attractive than the three- and five-cluster structures. This is consistent with the spectral clustering algorithm results (Figure 2). However, it should be noted that, out of the 1140 samples used to test the model, the state of 31 and 21 samples in the case of using the four- and six-cluster structures, respectively, was identified as undefined. This means that, in these patients, the type of cancer was not identified unambiguously, requiring further clinical studies to identify the disease type.  
The analysis of the simulation results also indicated the higher effectiveness of using the four-cluster structure for identifying the type of cancer. Analysis of the classification quality criteria values for samples corresponding to different types of cancer and the health state of patients (Figure 8a-c) suggested higher values and a better stability of these criteria when using the four-cluster structure compared to using other cluster structures. However, it should be noted that, when using the four-cluster structure, the state of a relatively large number of objects (31 out of 1140) was identified as undefined, which is undoubtedly a drawback of the RF-based model. Another significant drawback was the large amount of time required to train the model. The current training of data for each model (when using each cluster structure), took about 80 h.