esa-sceva/satcom-chunk-collection huggingface dataset:
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Position-Level Hybridization Method
The variance-covariance matrix \(\Sigma_{x}\) represents the accuracy of the WLS solution:  
\[\Sigma_{x}=\ \sigma_{0}^{2}\cdot(A^{\top}\cdot P\cdot A)^{-1} \tag{11}\]  
where the a posteriori variance factor \(\sigma_{0\text{P}}^{2}\) is computed as follows:  
\[\sigma_{0\text{P}}^{2}=\frac{v^{\top}\cdot P\cdot v}{n-m} \tag{12}\]  
Here, \(n\) is the number of observations and \(m\) is the number of unknowns. The diagonal elements of \(\Sigma_{x}\) are \(\sigma_{xx}^{2}\), \(\sigma_{yy}^{2}\), and \(\sigma_{zz}^{2}\) and they represent the variances in the local ENU coordinate system. Finally, the standard deviations are calculated as follows:  
\[\sigma_{x}=\sqrt{\sigma_{xx}^{2}},\ \sigma_{y}=\sqrt{\sigma_{yy}^{2}},\ \ \sigma_{z}=\sqrt{\sigma_{zz}^{2}} \tag{13}\]  
These values are used to assess and compare the positioning performance of the GNSS-4G/5G hybridization solution, the standalone GNSS solution, and the standalone 4G/5G solution. It is important to note that, in Equation (4), each observation value in the vector of independent terms \(b\) corresponds to a single epoch, and only one (\(x\), \(y\), \(z\)) coordinate is available per epoch for each source (GNSS or 4G/5G). As a result, a single epoch does not provide enough information to resolve the hybrid positioning system independently. Therefore, measurements from multiple consecutive epochs are accumulated and processed together to ensure system solvability and enhance the robustness.
## 3 Methodology
### Principal Component Analysis  
A set of eight texture features were calculated for each scattering element, and a total of 32 texture measures were obtained from the ALOS-2 and Sentinel-1 images. Performing land cover classification from these high dimensional datasets could be inefficient and time consuming. Thus, to reduce the dimensionality, the principal component analysis (PCA) was performed independently on each set of eight texture features. Since PCA computes the correlation between input bands and sorts them based on the amount of data variance [35], the first components contain the greatest variance of theinput variables [36]. In this research, three of the first components were selected, which contained almost 99 percent of the variation of the input elements and were used for the next stage. The reason on behalf the use of the first three principal components is explained further in Section 4.2. Therefore, two sets of SAR data were used for the supervised classification: (1) the backscatter values of the dual-polarization data, which contains two layers of HH + HV and VV + VH for the ALOS-2 and Sentinel-1; and (2) the layer stacking of the backscatter values and the first three principal components of the texture measures (PCT). The second dataset includes eight layers (HH, HV, \(PCT_{1,2,3}^{HH}\), \(PCT_{1,2,3}^{HV}\)) for ALOS-2 and eight layers (VV, VH, \(PCT_{1,2,3}^{VV}\), \(PCT_{1,2,3}^{VH}\)) for Sentinel-1.
## 1 Introduction  
The advancements in the casting technology though the 3D printing process satisfies the price competitiveness and improvement in product performance in the sand casting market, thereby rapidly competing with the classic casting method [1]. Functional inorganic materials that can be used at high temperatures, such as castables, using existing cement binders, require high-temperature thermal stability for casting. Mixing cement and foundry sand not only prevents toxic emissions from conventional 3D printing operations, wherein organic and inorganic binders are mainly used, but also simplifies the post-processes [2, 3].  
The application of 3D printing to sand casting can lead to further innovation in the sand casting resolution as high-temperature compatibility of existing alumina cement and precise shape implementation are steadily investigated through differentiation [4, 5, 6, 7]. Sand molds manufactured for casting using traditional casting methods can be easily accessed by applying conventional casting sand [8]. In 3D printing, mold manufacturing, whose resolution can be similar to that of commercial casting sand in the 3D printing of powder bed, has been commercialized and applied to large castings such as marine engine parts [1, 9]. Studies have been conducted to overcome the limitations of shape manufacturing using a finer process to ensure surface resolution and dimensional stability by reducing the material size [10, 11, 12]  
The resolution of 3D printing is ultimately determined by the uniformity and size (small) of the material, which can reduce the layer height. In recent studies, a uniform submicron particle size has been selected through a simple mixing process to solve the agglomeration of ceramic powder [13; 14]. Alumina, mullite, and fumed silica are commercially used as high-temperature casting sands [15]. Refractory calcium aluminate cement (CAC) exhibits high strength and quick setting at the submicron level [16]. While it is important to ensure sufficient casting mold strength, strength adjustment is required after melting as well to ensure that the mold can be removed and the casting shape can be fully obtained [8; 15]. Such intensity adjustment has become more necessary owing to the complex shape of the molded body in the increasingly detailed 3D printing market [17]. Additionally, it is essential to remove the mold without damaging the weak and fine parts of the inner cast [15; 18].  
In this study, the suitability of the handling strength of the mold was confirmed by comparing the formability of sand for casting at the submicron level considering powder bed 3D printing [5]. In order to select a sand with excellent castability and appropriate strength, the strength and phase changes due to heat treatment were checked. Finally, a sessile drop test was performed to verify the applicability of drop test to a powder bed, and the spreadability and strength were compared.
## 2 Related Research
[18] developed a projection navigation system that captures images at a rate of 60 frames per second. The projected image is calibrated through perspective transformation to guide the surgeon during the operation. In 2017, [PERSON] [19] used near-infrared light as the light source and fed the near-infrared image into a camera through the reflective function of a beamsplitter. After passing through a median filter to reduce noise and undergoing Otsu segmentation, the near-infrared image is transmitted to a projector, and ultimately projected onto the patient's dorsum of the hand. However, this method is limited by the high precision requirements of the instrument, as well as the information loss when the light passes through the beamsplitter. [PERSON] [20] proposed a system for the real-time visualization of veins based on near-infrared imaging, which displays the vein images in real-time on the monitoring screen. However, the method does not project the segmented image on the dorsum of hand; moreover, this method also lacks an effective design for the projection method. [PERSON] [21] proposed an in situ projection-based promtpter to assist workers with cognitive impairments. The product's location is obtained through a depth camera, and a homography matrix is used to project the related information onto the product. In 2018, [PERSON]. [22; 23] proposed a method for adjusting images using a distance sensor. After augmenting the acquired near-infrared images, the distance sensor is utilized to obtain the real-time distance from the hand's dorsum to the camera. With this distance, the projected image is finely tuned to synchronize with the object. This approach addresses the issue of changes in the homography matrix due to the hand's position change. Another method proposed by [PERSON] [24] uses fluorescence imaging with indocyanine green (ICG). After adjusting the optical paths of the camera and the projector to coaxial, the images are projected back to the surgical site in real time. Additionally, [PERSON]. [25] proposed a handheld projection device. In this device, the optical path of the camera runs parallel to that of the projector. After cropping the non-overlapping areas of the projector and the camera, the projected image is calibrated, and the medical staff are guided during surgery based on the overlapping area image.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics
#### 5.1.7 Wmd  
_WMD_ (Word Mover's Distance) is a metric for evaluating text document similarity [14], based on the Earth Mover's Distance. It measures the minimum distance to move embedded words from one document to match those of another using word2 vec, capturing semantic similarity for tasks like document retrieval and classification. Given two documents represented as normalized bag-of-words (nBOW) histograms \(d\) and \(d^{\prime}\), _WMD_ calculates the minimum travel distance between their embedded words.  
Formally, let \(X\) and \(X^{\prime}\) be the sets of embedded word vectors for documents \(d\) and \(d^{\prime}\), respectively. The _WMD_\(W(d,d^{\prime})\) is defined as the solution to the following optimization problem:  
\[\min_{T\geq 0}\sum_{i,j=1}^{n}T_{ij}c(i,j), \tag{23}\]  
subject to:  
\[\sum_{j=1}^{n}T_{ij}=d_{i}\quad\forall i\in\{1,\dots,n\}, \tag{24}\]  
\[\sum_{i=1}^{n}T_{ij}=d^{\prime}_{j}\quad\forall j\in\{1,\dots,n\}, \tag{25}\]  
here, \(T\) is a flow matrix where \(T_{ij}\) represents the amount of \"mass\" moved from word \(i\) in document \(d\) to word \(j\) in document \(d^{\prime}\). The cost \(c(i,j)\) is the Euclidean distance between the embedded word vectors \(x_{i}\) and \(x_{j}\):  
\[c(i,j)=\|x_{i}-x^{\prime}_{j}\|_{2}. \tag{26}\]  
The constraints ensure that the entire \"mass\" of each word in the source document is moved to the target document, and vice versa.
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Reducing Energy Consumption Using Green Energy  
UAVs are highly dynamic (X.1) in the three-dimensional space, and they may be spaced far apart, causing frequent link disconnection and network partitions that reduce route lifetime. Hence, high energy consumption is incurred to establish and select routes from time to time. Green energy-based mechanisms have been introduced to reduce energy consumption [26]. It helps to keep the energy by reducing the number of exchange messages between UAVs. Further investigation could be pursued to investigate how UAVs can collaborate for energy harvesting. It provides the longer life of network and route by keeping UAVs alive. Solar panels can be used to harvest energy in order to minimize the drainage of power in UAVs. Addressing this open issue helps to achieve lower energy consumption (P.2) and high route setup rate (P.4).
## 3 Results and Discussion
### ERT Field Acquisition and Data Analysis
#### 3.1.2 L2 Survey Line  
Figure 4 shows the result of the L2 survey line (RMS Error = 2.3%). It shows the same trend as Figure 3 because the L2 survey line is close to L1. The stratum is divided into two layers, in which the middle region has relatively high resistivity, while the rest is a stratum of lower resistivity. As the geology of this region is silty sand stratum, the geology is relatively loose and has higher water content. At 18-28 m of survey line and 2.5-5.5 m of depth, the resistivity value increases suddenly, the resistivity value of this region increases inwards, indicating this may be the tunnel location according to preliminary analysis. As shown in Figure, the tunnel may be located at 2.5 m underground, with a height of about 2.5 m and width of about 4 m, indicated by red dotted lines in Figure 4.
## 4 Convolutional Neural Network-Based Cooperative Spectrum Sensing
### Local Spectrum Sensing  
The considered CRN is assumed to be composed of \(K\) SUs. Each of them performs spectrum sensing independently using an energy detection algorithm, and then sends the outcome to the FC. Moreover, we assume that the status of the PU remains unchanged during the time slot. The hypothesis test statistics for local spectrum sensing at SU \(i\) can be formulated as follows [32]:  
\[\begin{cases}A:&x_{i}(t)=h_{iS}(t)+w_{i}(t),\\ \bar{A}:&x_{i}(t)=w_{i}(t),\end{cases};\;\forall i\in\{1,2,\ldots,K\}, \tag{5}\]  
where \(x_{i}(t)\) is the received signal by the \(i\)th SU in time slot \(t\), \(h_{i}\) denotes the channel gain of the link between the PU and the \(i\)th SU, \(s(t)\) denotes the PU signal, and \(w_{i}(t)\) is zero mean and unit variance additive white Gaussian noise (AWGN). Regarding energy detection, the observed energy at the \(i\)th SU is expressed as follows [33]:  
\[xE_{i}=\sum_{j=1}^{N_{i}}|x_{i}(j)|^{2};\;\forall i\in\{1,2,\ldots,K\}, \tag{6}\]  
where \(x_{i}(j)\) is the \(j\)th sample of the received PU signal at the \(i\)th SU, and \(N_{i}\) is the number of sensing samples during each sensing period. For simplicity, we assume that the number of sensing samples collected by each SU is the same for all the SUs. When \(N_{i}\) is sufficiently large (e.g., \(N_{i}\geq 200\)), \(xE_{i}\) can be approximated by a Gaussian random variable under the two hypotheses (\(A\) and \(\bar{A}\)) with mean \(\mu_{A}\), \(\mu_{\bar{A}}\) and variance \(\sigma_{A}^{2}\), \(\sigma_{\bar{A}}^{2}\), given as follows [34]:  
\[xE_{i}\sim\begin{cases}\mathcal{N}\left(\mu_{A}=N_{i}(1+\gamma_{i}),\sigma_{A} ^{2}=2N_{i}(1+2\gamma_{i})\right),&A,\\ \mathcal{N}\left(\mu_{A}=N_{i},\sigma_{\bar{A}}^{2}=2N_{i}\right),&\bar{A}, \end{cases} \tag{7}\]  
where \(\gamma_{i}\) is the average gain of the sensed channel in terms of signal-to-noise ratio (SNR). In this paper, we assume that \(\gamma_{i}\) follows a Gaussian distribution with mean \(\mu_{i}\) and variance \(\sigma_{i}^{2}\) as \(\gamma_{i}\sim\mathcal{N}\left(\mu_{i},\sigma_{i}^{2}\right)\).  
For a single-SU spectrum-sensing scheme, the local decision, \(D_{i}\), is given by  
\[D_{i}=\begin{cases}1,&if\;xE_{i}\geq\lambda_{i},\\ 0,&otherwise,\end{cases} \tag{8}\]  
where 1 and 0 are single-bit data that represent states \(A\) and \(\bar{A}\) of the primary user, respectively; and \(\lambda_{i}\) is a predefined decision threshold.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
###### Abstract  
In the prognostics health management (PHM) of rotating machinery, the accurate identification of bearing fault is critical. In recent years, various deep learning methods can well identify bearing fault based on monitoring data. However, facing changing operating conditions and noise pollution, the accuracy of these algorithms decreases significantly, which makes the algorithms difficult in practical applications. To solve this problem, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) is proposed for fault diagnosis. The proposed WMSCCN algorithm takes one-dimensional vibration signal as an input and no additional manual processing is required. In addition, the adaptive batch normalization (AdaBN) algorithm is introduced to further enhance the adaptability of WMSCCN under noise pollution and load changes. On generalization experiments under different loads, the proposed WMSCCN and WMSCCN-AdaBN algorithms achieve average accuracy rates of 96.44% and 97.44%, respectively, which is superior to other advanced algorithms. In the noise resistance experiment, the proposed WMSCCN-AdaBN can maintain a 92.3% diagnostic accuracy in a strong noise environment with a signal to noise ratio (SNR) = \(-4\) dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels. Two experiments have effectively verified the validity and generalizability of the proposed model.  
multi-scale convolution; capsule network; fault diagnosis; adaptive batch normalization Article
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 4 System Design
### Workload Distribution and Overload Avoidance Policy for Workload-Admission Control
The acceptance decision is based on the service consumption comparison. The acceptance decision of a cloud decision is dependent on the cost of accepting the request (resource consumption) and the maximum throughput of the system. Therefore, the choice between \(T_{c}\) and \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline _r,t_ & Row vectors \\ \(T\) & Total number of available compute nodes \\ \(T_{c}\) & Cumulative tasks \\ \(R_{c}\) & Resource utilization \\ \(P_{t}\) & Number of permissible tasks \\ \(R_{t}\) (\(c\)) & Total response time \\ _Wt_ (\(Q_{improgens}\)) & Waiting time of in-process requests of cluster \(Q\) \\ _Wt_ (\(Q_{waiting}\)) & Waiting time of waiting requests of cluster \(Q\) \\ _Wt_ (\(Q_{threshold}\)) & Threshold value for waiting time of cluster \(Q\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Workload distribution and overload avoidance policy analysis. Figure 2: Analysis of resource provisioning in Bouncer. \(R_{t}(c)\) ensures a ratio of \(W_{t}(Q_{threshold})\) for a sequence of admission control services. Therefore, the in the worst-case scenario, the number of accepted service requests would be smaller than the required number of requests. The rationale behind the performed study is to derive strategies for developing demand patterns. We summarize the approach in Algorithm 1. ```
1:Input: reqQueue: The queue of requesting services; vmQueue: The list of available VMs; reqResource:
2: The required resources for executing a VM request; vmResource: Cumulative available VM resource;
3: waitQueue:The waiting queue of (not satisfactory) applications. 4:Output: Capacity-aware admission control and forwarding decision
5: /* We implement 3 major conditions to be satisfied before an application can be allocated to a VM. The wait and drop Queues amass the pending and rejected applications */
6:whilereqQueue!= NULLdo
7: Request\(\leftarrow\)DeQueue(reqQueue);
8:ifvmQueue == NULLthen
9: dropReq (request);
10: continue;
11:endif
12:whilevmQueue!= NULLdo
13:vm\(\leftarrow\)getVM (vmQueue);
14:/* We consider required resources of a VM as a composite of CPU, Memory and Storage resources. VM must ensure that it have enough resources to handle the resource demands */
15:ifvmResource[vm] >reqResource[request]then
16: allocateResource(vm, request);
17: DeqQueue(vmQueue, vm);
18:/*Traverse VM queue, and consider next resource demand*/
19:break;
20:endif
21:vmQueue\(\geq\)vmQueue.next;
22:/*If none of available VM can satisfy the service requirements, append requests */
23:ifvmQueue==NULLthen
24:EnQueue(waitQueue, request);
25:endif
26:endwhile
27:endwhile
```  
**Algorithm 1**Workload distribution and overload avoidance policy.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 3 Optimization Model  
Researchers often focus on cost and power supply stability in the optimization of microgrid configurations, while neglecting mobility. Mobility is a crucial metric for emergency response teams. Teams with high mobility requirements are unable to carry an excessive amount of energy, while the stability of power supply necessitates carrying an adequate number of power sources to ensure continuous and stable supply. Clearly, the mobility index conflicts with the power supply stability index. The problem that is addressed in this paper involves obtaining microgrid configuration solutions using a multi-objective evolutionary algorithm with classification based on preselection. We select solutions that achieve a compromise between mobility and stability indices, which effectively balance mobility and power supply stability.
## 4 Discussion
Clearly, reducing velocity losses allows for higher-quality repetitions, leading to greater speeds attained during the exercise session. [PERSON] et al. [18] explored the effects of applying velocity losses of 20% and 40% in young males on performance enhancements, changes in muscle fiber characteristics, and cross-sectional area. Their findings revealed that the group experiencing a high velocity loss of 40% exhibited a significant decrease in type IIX fibers, which could adversely affect strength and power development as well as lengthen recovery times [34]. In addition, [PERSON] et al. [14] demonstrated that their low velocity loss group (VL10%)experienced significant improvements in strength, jumping, and sprinting performance, which were accompanied by increased neural activation of the agonist muscles involved in the exercises. In contrast, neural activation remained unchanged for the VL30% and VL45% groups. Collectively, these findings suggest that establishing a low velocity loss limit (5-15%) during each exercise set leads to both effective and efficient training stimuli, promoting significant neuromuscular adaptations while requiring fewer repetitions and inducing lower levels of fatigue (mechanical and physiological stress) compared to training volumes associated with higher velocity loss thresholds. This could be of particular importance during soccer season, when the general objective of resistance training is to induce long-term benefits on performance outcomes while maintaining peak performance during subsequent field soccer practice. Indeed, resistance training with heavy loads and near failure repetitions per set has been shown to induce greater short-term deterioration in jump, sprint, and change of direction compared to lower repetitions or %VL in the set [35; 36]. These physiological differences reinforce the present study's findings and highlight the advantageous use of velocity-based training to optimize strength and power adaptations in elite youth soccer players. Several limitations of this study should be noted. The participant sample is limited to players from Serbia, and we do not know whether athletes from other countries have the same characteristics. The training load was manipulated solely for the back squat; however, this approach is consistent with previous velocity-based training (VBT) studies. Adjusting the load for other lower-body exercises in the training routine, such as the deadlift, forward lunge, and hip thrust, using velocity thresholds was not deemed appropriate, as maximizing concentric velocity was not the primary training objective for these movements. Additionally, we did not monitor the load used during regular soccer practice sessions conducted among all three groups with their respective coaches, which could have impacted the training adaptations. Lastly, this study only lasted four weeks, highlighting the need for comparative investigations using strength modalities over longer durations.