esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Dig Sequence Effects during Large-Scale Excavation
## 6 Conclusions  
In this study, the effect of excavation sequence on the stability of irregular large-scale rectangular deep foundation pit was investigated through numerical simulation, the optimal excavation sequence scheme was determined, and the main conclusions were obtained as follows:  
1. Different excavation sequences can significantly affect the stability of a foundation pit, for the displacement, in which the maximum cumulative value of the displacement under scheme E is only 67.5% of that under scheme A. In addition, the excavation sequence and excavation phases lead to a change in the type of horizontal displacement of the foundation pit.
2. We defined a foundation pit excavation safety coefficient Kes, which comprises six factors that were scored using the min-max normalization method, and we obtained the optimal excavation sequence scheme, i.e., scheme E: \"Excavate from the two ends of the  
Figure 11: Horizontal displacement of the pit after each layer of excavation for scheme E. (**a**) History profile 1. (**b**) History profile 2. (**c**) History point 3. (**d**) History profile 4.  
foundation pit to the center at the same time, and then after completing the excavation of the first layer, then excavate from the center of the next layer to the two ends\".
3. Irregular-shaped foundation pits can also lead to local differences in the foundation pit's stability; for example, in scheme E, the maximum displacement of the wide end reaches 221% of that the narrow end, but the length of the wide end is only 108.9% of that of the narrow end. Therefore, attention should paid to the reinforcement of weak areas when implementing supports.  
Conceptualization, D.L., Y.C., B.D., Z.W. and H.L.; methodology, D.L. and Y.C.; software, D.L.; validation, Y.C.; formal analysis, B.D.; investigation, D.L.; resources, Y.C. and Z.W.; data curation, Z.W.; writing--original draft preparation, D.L.; writing--review and editing, D.L. and Y.C.; visualization, B.D.; supervision, Z.W. and H.L.; project administration, Y.C. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52004130), the Provincial Natural Science Foundation of Hunan (2022)J40601, 2022)J40373), the China Postdoctoral Science Foundation (2021M693799), and the Postgraduate Scientific Research Innovation Project of Hunan Province (QL20220213).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## Materials and Methods
### Investigation of Platelet Aggregation  
To demonstrate that the selected frequency and intensity of ultrasound induce platelet activation in a dose-dependent manner, a single individual's blood was analyzed utilizing a platelet aggregometer. Two blood samples were collected for testing purposes. One sample served as the control, without any ultrasound exposure, and instead was treated with epinephrine (adrenaline) to induce platelet aggregation. Another sample was divided into 4 tubes and exposed to ultrasound for varying durations and intensities. It was exposed to ultrasound for 90 s. or 180 s., and after exposure, it was treated with epinephrine to induce platelet aggregation. The ultrasound tests were carried out at different ultrasound signals, i.e., some samples were tested at an electric power of 35 W (US intensity \(\sim\)50-70 mW/cm\({}^{2}\)), as shown in Table 1, and others were tested at 60 W (US intensity \(\sim\)100-150 mW/cm\({}^{2}\)). The time and frequencies of 44 kHz were the same to ensure that the ultrasound effect would not be harmful. The platelet aggregation test was conducted at the Laboratory of Molecular Cardiology, Institute of Cardiology, Lithuanian University of Health Sciences, located in Kaunas, Lithuania. The test followed the established classical Born method and utilized a semi-automatic CE IVD certified TA-8V platelet aggregometer from SD Medical (Frouard, France). Platelet aggregation was assessed by comparing the intensity of light transmission between platelet-rich and platelet-poor plasma samples after induction with epinephrine (adrenaline). The transmission of platelet-poor plasma was considered as 100%. The final concentration of epinephrine was 10 \(\upmu\)M (Chrono-Log, Havertown, PA, USA). Preparation of platelet-rich plasma was carried out by centrifuging whole blood at 100\(\times\)\(g\) for 15 min. Platelet-poor plasma was prepared by centrifuging platelet-rich plasma at 1000\(\times\)\(g\) for 30 min. An AFI LISA 2.5 L refrigerated centrifuge (Chateau-Goutier, France) was used to prepare blood samples. The aggregation of platelets was measured as % Agr.
## 3 Results
### RQ3--How Have These Proposals Contributed to Addressing Different Levels of Violence?
#### 3.3.1 Monitoring Violence Actions  
The citizen security taxonomy in Figure 5 classifies violence actions and crimes based on their nature and severity, providing a hierarchical and structured view of these phenomena. Violence actions are diverse; however, for the purposes of this study, five types of violence are considered: domestic violence, gender-based violence [67], child and adolescent violence, sexual violence [68], and street violence actions (such as vandalism). Each of these actions can cause physical and/or psychological harm to individuals or groups, either directly or indirectly.  
Among the analyzed studies, three main studies (e.g., S28, S42, S45) focused on monitoring street violence. These studies addressed vandalism and public disturbances, tracking their occurrence and severity in urban areas. They utilized image processing, video surveillance, and real-time monitoring to detect violent incidents in public spaces, emphasizing the identification of public disturbances and confrontations, which contribute to enhancing public safety and enabling timely interventions.  
The analyzed proposals apply deep learning to enhance urban security through artificial intelligence (AI) and data analytics. Generic and hybrid CNNs are commonly used to improve crime detection accuracy. For example, property crime detection is explored in S5, S13, and S33, focusing on electricity theft and unauthorized property intrusion, demonstrating how technology aids crime prevention. Additionally, several studies highlight the integration of deep learning and IoT in urban security, illustrating the diversity of threats and the technological solutions employed.  
Physical violence in public spaces is analyzed in various studies, focusing on automatic detection of assaults and weapons (e.g., S28, S26) and urban disturbances with intelligent classification of critical events (e.g., S42). Other studies highlight intruder identification and hazardous object detection in restricted areas (e.g., S29).  
Figure 5: Taxonomy of violent actions and crimes by severity of the event [16].
# Data Analysis for Information Discovery
## 8 Experiments and Results
### Room Occupancy Estimation Dataset
#### 8.3.2 Clustering and Silhouettes Using Normalized Data  
The original dataset (without the features containing NaN values) was normalized between 0 and 1. Applying PCA to this dataset, losing less than 2% of the total variation in the data, produced a dataset with nine dimensions.  
The following Tables 16-18 show the obtained results in terms of silhouettes varying the number of clusters from 10 to 50 (see columns). Each row represents the percentage of points with a silhouette greater than a given threshold, that is 0.7, 0.8, and 0.9.  
In these experiments, the results obtained using normalized data seem to be worse than those obtained with the raw data.
## 3 Online Processing Parameters Prediction and Reverse Design Framework
### Concept Drift Detection  
Concept drift detection refers to techniques and mechanisms that characterize and quantify concept drift by identifying change points or change intervals. Common concept drift detection methods are categorized into the following three types: model-error-rate-based methods, data-distribution-based methods and multiple-hypothesis-testing-based methods [11]. Among them, data-distribution-based methods use a distance function to quantify the difference between historical data distributions and new data. If the difference between the two proves to be statistically significant, it indicates that concept drift has occurred. Classical data-distribution-based detection algorithms include model-capability-based drift detection (SCD) [23], a least squares density difference change detection test (LSDD-CDT) [24] and others [12; 25; 26].  
[PERSON] et al. [27] proposed a data-distribution-based drift detection algorithm called equal intensity k-means space partitioning (EI-kMeans) for concept drift detection via histograms. Traditional data-distribution-based algorithms for detecting distribution-based drift seek to develop a novel test statistic to quantify the disparity between two distributions and craft a tailored hypothesis test to assess the significance of drift [28]. Conversely, EI-kMeans concentrates on the efficient transformation of multivariate samples into multinomial distributions, subsequently employing established hypothesis tests for concept drift detection. Utilizing [PERSON]'s chi-square test [29] as its hypothesis testing method, EI-kMeans enables the direct calculation of drift thresholds from the chi-square distribution, facilitating online implementation. In this paper, if the p-value, which is the result of the [PERSON]'s chi-square test, is greater than 0.05, the test data are considered to have experienced concept drift.
## 2 Cardiovascular Disease
### Coronary Artery Disease
#### 2.1.7. Current Treatments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Medications** & **Medicines** & **Comments** \\ \hline \multirow{4}{*}{Antiplatelets} & Acetylsalicylic acid (ASA) & A low dose of ASA is recommended as a blood thinner and to lower the risk of CAD events. Patients with CVD history are advised to use it lifelong. \\ \cline{2-3}  & Clopidogrel & It prevents platelets from bonding together, especially in patients who are at a high risk of CAD. \\ \hline \multirow{4}{*}{Cholesterol-lowering drugs} & Statins & Statins are widely used to block the pathway by which the liver produces cholesterol via the inhibition of HMG-CoA reductase. (Hydroxymethylglutaryl-coenzyme A) \\ \cline{2-3}  & & It inhibits cholesterol absorption in the intestine and biliary tract. This helps reduce LDL cholesterol significantly. \\ \cline{2-3}  & & Ezetimibe can be prescribed in combination with statins [73]. \\ \hline \multirow{4}{*}{Antihypertension: beta blockers} & Acebutolol, bisoprolol, and metoprolol & Beta blockers inhibit adrenaline and noradrenaline which result in slowing the heart rate and reducing blood pressure. These drugs are convenient for hypertensive patients with CAD as they relieve chest pain (angina). \\ \cline{2-3}  & & CCB causes the heart to relax via blocking cell membrane calcium gates and thus preventing calcium from entering cardiac cells. This pathway causes blood vessels to widen and lower the blood pressure. \\ \hline \multirow{4}{*}{Antihypertension: ACE inhibitor} & Benzaepril, captopril, and enalapril. & ACE inhibitors stop the conversion of angiotensin I into angiotensin II by blocking the angiotensin-converting enzyme. This action reduces the blood pressure and additionally prevents VSMCs’ remodelling process [74]. \\ \cline{1-1} \cline{2-3}  & & ACE inhibitors stop the conversion of angiotensin I into angiotensin II by blocking the angiotensin-converting enzyme. This action reduces the blood pressure and additionally prevents VSMCs’ remodelling process [74]. \\ \cline{1-1} \cline{2-3}  & & ACE inhibitors stop the conversion of angiotensin I into angiotensin II by blocking the angiotensin-converting enzyme. This action reduces the blood pressure and additionally prevents VSMCs’ remodelling process [74]. \\ \cline{1-1} \cline{2-3}  & & ACE inhibitors stop the conversion of angiotensin I into angiotensin II by blocking the angiotensin-converting enzyme. This action reduces the blood pressure and additionally prevents VSMCs’ remodelling process [74]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Commonly used medications for the treatment of CAD.
## 3 Conventional BEMS Techniques
### PID Control  
All three types (proportional, integral, and derivative) of action are utilized in most digital controllers to incorporate advantages such as removing offset and speeding up the response of the control function. In a nutshell, the integral control function tends to destabilize the system, whereas the derivative control function tends to reinforce it. The integral function is frequently used to reduce or eliminate the offset of proportional control and to provide more precise control. A typical structure of a PID controller is shown in Figure 5[60]. The time domain function of PID control is shown in Equation (1).  
Figure 4: A typical structure of a thermostat.  
The transfer function of PID control can be written in \"Ideal form\" and \"parallel form\", respectively, as shown in following Equations (2) and (3).  
\[PID(s)=K_{P}\bigg{(}1+\frac{1}{T_{i}s}+T_{d}s\bigg{)} \tag{2}\]  
\[u(t)=K_{P}e(t)+\frac{1}{K_{i}}\int\limits_{0}^{t}e(\tau)d\tau+K_{d}\frac{de( \tau)}{d\tau} \tag{3}\]  
where, \(K_{P}\), \(K_{i}\), and \(K_{d}\) denote proportional gain, integral gain, and derivative gain, respectively. \(T_{i}\) and \(T_{d}\) are integral and derivative time constants, respectively. Since P, PI, and PID controllers are closed loop/feedback controls that have constant parameters and do not have a direct knowledge of the system to be regulated. When utilized alone, they give poor control performance for noisy and non-linear processes with long-time delays [61]. In general, the PID controller has been effectively used in a variety of building sections, including lighting management [62], tracking performance improvement [63], and energy consumption reduction. There is no doubt that PID performance is more efficient than thermostat [64]; however, there is still a demanding task of choosing the suitable settings of \(K_{P}\), \(T_{i}\), and \(T_{d}\), respectively. In addition, when integral functions are introduced to eliminate the offset of proportional control to obtain a more accurate control of variables and objectives, their effect might cause the system to oscillate and deteriorate control [49]. To tackle these issues of PID controllers, optimization control strategies in BEMS are explained in Section 5.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
###### Abstract
This paper considers the situation of small cells deployment in an evolved wireless network when due to different technical and economic reasons the backhaul connection of the evolved small cell is implemented using the shared links of the macro cells covering the small cell (i.e., in-band and/or out-band self-backhauling). To provide the backhaul capacity required by the evolved small cell in discussion the aggregation of several macro cell links is considered, a process that requires splitting the traffic over the available links modeled as communication tunnels, which is a resource usage optimization problem, while specific constraints are imposed. Considering the ability of the GT-based algorithms of solving optimization problems the paper proposes several GT-based LB algorithms over the macro cell links providing the backhaul connectivity. The proposed algorithms are adapted to the specific case of LB over virtual communication tunnels with specific definitions of the games and specific QoS parameters which should be ensured. The performance of the proposed GT-based LB algorithms and of some reference LB algorithms are assessed by extensive computer simulations. The obtained results show the superiority of LB algorithms which model the LB process as a multiplayer game based on a sealed-bid auction process compared to non-GT-based algorithms and non-cooperative GT-based algorithms. The paper also considers scenarios when several small cell APs are deployed and proposes GT-based solutions for user clustering, i.e., the selection of the user groups that connect to each AP. The goal is the LB between the deployed APs to ensure the QoS requirements on the AP-user links, and of course to ensure the global end-to-end QoS.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### Semantic Similarity Matrix Updating
#### 3.4.1 Similarity Matrix Construction  
To comprehensively consider cross-modal similarity relationships, we first fuse the similarity matrices from image modality and text modality:  
\[\mathbf{S}^{f}=\alpha_{1}\mathbf{S}^{v}+(1-\alpha_{1})\mathbf{S}^{t}, \tag{2}\]  
where \(\mathbf{S}^{f}\) is the fused similarity matrix, \(\mathbf{S}^{v}\) and \(\mathbf{S}^{t}\) represent the image and text similarity matrices, respectively. \(\alpha_{1}\in[0,1]\) is a hyperparameter to balance them. Each element in \(\mathbf{S}^{v}\) and \(\mathbf{S}^{t}\) is computed by Cosine similarity. Taking image modality as an example:  
\[\mathbf{S}^{v}_{ij}=cos(\mathbf{v}_{i},\mathbf{v}_{j})=\frac{\mathbf{v}_{i}^{T}\mathbf{v}_{j}}{ \|\mathbf{v}_{i}\|_{F}^{2}\|\mathbf{v}_{j}\|_{F}^{2}}, \tag{3}\]where \(\mathbf{v}_{i}\) and \(\mathbf{v}_{j}\) denote the feature of the \(i\)-th and \(j\)-th image. To rise the accuracy of similarity measurement without supervision, we design a filter-and-augment strategy to refine the similarity matrix, shown as follows:  
\[\mathbf{S}^{r}=\begin{cases}sgn(\mathbf{S}^{f}),&\left|\mathbf{S}^{f}_{ij}\right|>\eta_{1} \\ 2\times sigmoid(2\times\mathbf{S}^{f})-1+\mathbf{I},&\text{otherwise}\end{cases}, \tag{4}\]  
where \(\mathbf{S}^{r}\) denotes the refined similarity matrix, \(\eta_{1}\) is similarity threshold. the sign function \(sgn(\mathbf{S}^{f})\) indicates if \(\mathbf{S}^{f}_{ij}>\eta_{1}\), \(\mathbf{S}^{f}_{ij}=1\); if \(\mathbf{S}^{f}_{ij}<-\eta_{1}\), \(\mathbf{S}^{f}_{ij}=-1\). Meanwhile, inspired by reference [28], we use the non-linear function \(sigmoid(\cdot)\) to compress the remaining values to further expand the distance between similar and non-similar instances. The coefficient 2 of \(\mathbf{S}^{f}\) is to control the linear compression, then \(2\times sigmoid(\cdot)-1\) maps the compressed value to \([-1,1]\). \(\mathbf{I}\) is an identity matrix to maintain the similarity between instance pairs.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 4 Relative State Measurement Technology Trends and Optimization
### Development Trend
The system no longer relies on the inherent relative state measurement procedure to realize formation and instead adopts an intelligent learning method to complete relative identification measurements, as well as task objective and self-control optimization [97; 98]. The precision formation flying of spacecraft as an aerospace engineering mission is inevitably subject to errors or failures in actual measurement and control. Robust relative measurement and formation control under partial failure is also an advantageous development direction. The stability of formation measurement is mainly achieved through two aspects, the optimized design of hardware against radiation and noise [99; 100], and the algorithm of robust measurement and control under partial failure [82; 83; 101]. The software-defined distributed spacecraft formation flying relative state measurement refers to the calculation of the center and the software as a means to complete the measurement function [102]. Functions such as communication and relative measurement load, which are traditionally realized by subsystems, are realized via software [103]. From the overall perspective of the spacecraft, the relative measurement load, control subsystems, and other actuators are connected, as a whole, through software. It relies on an efficient computer processing platform and can realize plug-and-play measurement loads through an open software architecture. By loading application software on demand and quickly reconfiguring the spacecraft, it is possible to flexibly add or remove the formation members.
## 4 Conclusions  
For a loose integration of GNSS and SM with uncertain system noise characteristics, the SKF technology no longer satisfies the requirement of time-varying system noise variance. In this study, VC-AKF is proposed to construct the loose integration system for obtaining more robust results in real time, and the adaptive process is improved by the covariance compensation. It can remove the baseline shift of SM and weaken the influence of time-varying system noise variance. The effectiveness and superiority of the presented VC-AKF are verified by a numerical simulation and a practical seismic experiment from the 2017 Ms 7.0 Jijuzhaigou earthquake. The results validate that the presented VC-AKF can improve the precision of state estimation and restore seismic broadband signals realistically, which is of great significance for earthquake early warning and rapid response. Although the algorithm presented in this study continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time, it is important to specifically note that it has not separately or individually processed various types of noise, such as the separation and treatment of random noise, which represents a limitation of this study, with further exploration needed in future research.  
Figure 6: Power spectral densities (PSDs) of the GNSS-derived (purple), SM-derived (green), VC-AKF-derived (blue), and SKF-derived (red) displacements.  
**Author Contributions:** Conceptualization, R.W., H.W. and R.S.; methodology, R.W., H.W. and R.S.; validation, R.W. and H.W.; formal analysis, H.W. and R.S.; writing--original draft preparation, R.W., H.W., R.S. and J.K.; writing--review and editing, R.W., H.W. and R.S.; supervision, R.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (grant 42201488) and the Beijing Key Laboratory of Urban Spatial Information Engineering (grant 20230103).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.