esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 5 Health Monitoring Using Machine Vibrations  
Power sources are important devices that need maintenance requirements to be estimated. Vibration occurs because of magnetic fields generated during power conversion. Harmonic vibration occurs without an external excitation device. The vibration characteristics depend on the efficiency of the power conversion. The rotation of elements also generates harmonic vibrations without excitation devices. Because rotating components have a strong influence on the vibration characteristics, the resulting signal processing needs to consider relevant variations.  
The ambient excitation has been used to monitor large structures. Structural vibration is induced by variations in the surrounding environment (e.g., running vehicles, wind and interaction with nearby structures). The calculation of the vibration response requires transient vibration energy transfers to be considered. In addition, the vibration characteristics of the surrounding environment should be understood and correlated with the monitored structure.  
Excitation occurs throughout the frequency band when the system is excited by external forces amplified by an impact or random signal. The excitation can be performed at a predetermined frequency to observe the behavior in a specific mode. This direct excitation provides an advantage in terms of the interpretation of results and assumptions for the desired mechanical deformations.  
[PERSON] et al. [96] reviewed the health monitoring of machines using deep learning. They explained each deep learning technique and described an example application. The purpose of their paper was to provide a vibration approach to monitoring structures and to determine the appropriate method according to the cause and interpretation of the vibration response.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 3 GSFedSec: GS-Based SA for Privacy Preservation in FL
### System Setup
#### 3.1.3 Signature Generation  
After the edge device receives \(g_{pk}\) and \(x_{i}\) from the admin, it can send messages by computing its GS. To formulate its GS, it must first compute the tracing, binding, and  
Figure 4: GSFedSec: Group signature-based secure aggregation for privacy preservation in federated learning.  
signing variables. To compute the tracing variables, we set \((c_{1},c_{2},c_{3})\) and two helper values \((\delta_{1},\delta_{2})\), as given in Equations (2a) and (2b):  
\[c_{1}\gets u^{\alpha},\ \ c_{2}\gets v^{\beta},\ \ c_{3}\gets Ah^{ \alpha+\beta}, \tag{2a}\] \[\delta_{1}\gets x\alpha,\ \ \delta_{2}\gets x\beta\in Z_{p}. \tag{2b}\]  
These tracing variables are used to detect the actual signer of the message. This tracing activity can be performed by the admin using the following formula given in Equation (3):  
\[A_{i}\gets c_{3}/(c_{1}^{\delta_{1}}c_{2}^{\delta_{2}}). \tag{3}\]  
This \(A_{i}\) has the corresponding value \(x_{i}\) (identity of the client) in the membership table, which is stored by the admin. Whenever required, the admin extracts the \(x_{i}\) from the table and identifies the actual signer. If the client does anything unwanted or shows malicious behavior, it can be removed by the group admin, and the group keys are updated accordingly [26]. Hence, adding the traceability feature in the group signature always keeps the clients in check. Hence, it is important to add tracing variables to the signature to make the signer traceable by the admin.  
Subsequently, the device chooses \(b_{x}\), \(b_{\beta}\), \(b_{x}\), \(b_{\theta 1}\), and \(b_{\theta 2}\) as binding values; these are computed as follows:  
1. Compute binding variables (Equations (4a)-(4c)): \[B_{1}\gets u^{d_{a}},\ \ B_{2}\gets v^{d_{\beta}},\] (4a) \[B_{3}\leftarrow\delta(c_{3},g_{2})^{d_{x}}\delta(h,w)^{-b_{\alpha}-b_{\beta}} \delta(h,g_{2})^{-b_{\delta_{1}}-b_{\delta_{2}}},\] (4b) \[B_{4}\gets c_{1}^{b_{x}}u^{-b_{\delta_{1}}},\ \ B_{5}\gets c_{2}^{b_{x}}v^{-b_{ \delta_{2}}}.\] (4c)
2. Compute challenger (Equation (5)): \[c\gets H(\omega,c_{1},c_{2},c_{3},B_{1},B_{2},B_{3},B_{4},B_{5})\in Z_{p}.\] (5)
3. Compute signing variables (Equations (6a) and (6b)): \[s_{x}\gets b_{x}+c\alpha,\ \ s_{\beta}\gets b_{\beta}+c_{\beta}\ \ s_{x}\gets b_{x}+cx,\] (6a) \[s_{\delta_{1}}\gets b_{\delta_{1}}+c\delta_{1},\ \ s_{\delta_{2}}\gets b_{\delta_{2}}+c\delta_{2}.\] (6b)  
Therefore, the GS can be given as formula given in Equation (7):  
\[\sigma_{\text{GS}}\leftarrow(c_{1},c_{2},c_{3},c,s_{x},s_{\beta},s_{x},s_{ \delta_{1}},s_{\delta_{2}}). \tag{7}\]
## 3 Model Development
### Prediction of the Induction Time without AS (t0A)
It can be assumed that clusters flowing in the feed solution result in \(u_{d}<<u_{f}\). This assumption can be verified by rearranging Equation (6) to obtain Equation (7):  
\[u_{d}/u_{f}=f/1+f\text{ where }f=(dm/dt)/6\pi\eta R \tag{7}\]  
Equation (4) shows that \(dm/dt\) is proportional to R\({}^{2}\) through A, and \(f\) in Equation (7) is proportional to R with a proportionality coefficient \(\alpha\) of the order \(10\,1/m\). As the cluster size is less than \(1\,\mu m\)[20], the resulting value of \(f\) is much smaller than \(1\). Therefore, Equation (7) yields \(u_{d}/u_{f}<<1\), which justifies the neglecting of \(u_{d}\) in Equation (6). By neglecting \(u_{d}\) relative to \(u_{f}\) in Equation (6) and combining it with Equation (5), Equation (8) is obtained. \[dm/dt=4F_{s}/\pi u_{f} \tag{8}\]  
The shear force \(F_{s}\) is derived from Equations (4) and (8). \[F_{s}=\pi u_{f}\text{ }k_{c}A(C_{f}-C_{e})^{2}/(4+\pi u_{f}k_{s}) \tag{9}\]  
The rate of mass growth of the cluster, \(dm/dt\), is obtained from Equations (4) and (9). \[dm/dt=4k_{c}A(C_{f}-C_{e})^{2}/(4+\pi u_{f}k_{s}) \tag{10}\]  
The variables m and A on both sides of Equation (10) are time dependent as they both depend on the number n(t) \(\geq 1\) of molecules in a growing cluster with size and time. The mass of the cluster composed of n molecules is given by the following:  
\[\mathrm{S_{a}}=\mathrm{C_{f}}/\mathrm{C_{e}} \tag{2}\]0  
The cluster's volume V(n) is approximated by considering n hard spheres randomly packed with a bulk density of 0.635 [35]. \[\mathrm{S_{a}}=\mathrm{C_{f}}/\mathrm{C_{e}} \tag{2}\]1  
where \(V_{m}\) is the molar volume. Although n is a discrete variable, the cluster's packed volume V(n) in Equation (12) is considered a continuous volume. This assumption can be justified by analyzing a single average cluster that represents the growth process of the cluster population in the feed at a specific time t. Using Equation (12) the cluster surface area is calculated by the following:  
\[\mathrm{S_{a}}=\mathrm{C_{f}}/\mathrm{C_{e}} \tag{2}\]2  
The cluster surface area A in Equation (13) is time-dependent through the cluster size n(t), represented as A = A(n(t)) = A(t). It is specified using Equations (12) and (13). \[\mathrm{S_{a}}=\mathrm{C_{f}}/\mathrm{C_{e}} \tag{2}\]3  
In the analyzed datasets, the measured induction time of RO membranes, \(t_{0A}\), results from the initial flux decline due to partial-scale coverage of the membrane surface. Since for n \(>n_{c}\), the cluster size grows spontaneously [1] and \(t_{c}=t(n_{c})\), it is assumed that in RO systems, \((t_{0A}-t_{c})/t_{0A}<<1\). This means that practically \(t_{c}\sim 10A\). By combining Equations (10), (11) and (13) and with n = \(n_{c}\), and t = \(t_{0A}\), \(t_{0A}\) is obtained as follows:\[\mathrm{S_{a}}=\mathrm{C_{f}}/\mathrm{C_{e}} \tag{2}\]4  
\(\mathrm{t_{0A}}\) in Equation (15) predicts the induction time with the shear rate constant \(\mathrm{k_{s}}\) as a single adjustable parameter of RO systems without AS.
## 4 Experimental Evaluation
### Dataset  
In order to evaluate the prediction performance of our system, we used the StudentLife [32] dataset. The StudentLife dataset was collected by a research team from Dartmouth College within the activities of the StudentLife project, a research initiative aimed at understanding the daily activities, behaviors, and well-being of college students. This dataset incorporates information obtained from smartphone sensors and wearable devices, encompassing data on physical activity, location, sleep patterns, modalities, and communication. Additionally, it includes survey responses addressing different health parameters, including sleep quality.  
Approval of all ethical and experimental procedures and protocols of the StudentLife dataset was granted by the Institutional Review Board at Dartmouth College [32]. The data collection lasted 10 weeks and was carried out with 48 individuals (30 undergraduates and 18 graduate students; 10 females and 38 males) using Android smartphones. Smartphone data were automatically gathered without user input and sent to the StudentLife project cloud infrastructure. The app automatically inferred participants' physical activity, sleep duration, and sociability, including the number and duration of conversations. The sleep recognition algorithm achieved 95% accuracy in correctly recognizing bedtime, with an error \(\pm\)25 min. The app also gathered data from different sensors, including inertial sensors, location/co-location/proximity, and audio and light sensor readings. Moreover, participants were prompted to respond to various Ecological Momentary Assessment  
Figure 9: Smartphone app explanation.  
(EMA) questions related to different health dimensions, including perceived sleep quality. The EMA reports, supplied by a medical doctor and psychologists from the research team, were administered multiple times a day, with an average of 8 EMAs per day. Different incentives were provided to participants to promote compliance and ensure data quality. The acquired data were fully anonymized to enforce data privacy and were stored on secure servers. Overall, the total size of the dataset is 52.6 GB. The dataset is represented in tabular format, where each row corresponds to a sensor event. The dataset encompasses a comprehensive array of information, comprising approximately 23 million accelerometer events, 99 million audio-related events, 79 thousand conversation events, 7 thousand events associated with light levels, and 9 thousand events related to phone usage. The dataset is publicly available at [[https://studentlife.cs.dartmouth.edu/](https://studentlife.cs.dartmouth.edu/)]([https://studentlife.cs.dartmouth.edu/](https://studentlife.cs.dartmouth.edu/)) (accessed on 29 January 2024).
## 3 Joint Optimization of Altitude and Energy Beamforming for a Single EHD  
In this section, we first solve the problem \(\mathbf{P}_{2}\) in a single-EHD scenario by deriving energy beamforming in closed form. We then devise a method to jointly optimize altitude and energy beamforming. Next, we develop an efficient low-complexity algorithm that yields the near-optimal altitude of the UAV.
## 2 The Bus Synchronization Problem
### The Bi-Objective Bus Synchronization Problem in Montevideo
Alternatively, passengers can opt for multi-leg trips, also known as transfers [23], within a specified time frame (e.g., one hour or two hours) using the STM card. Within this context, the target Bus Synchronization Problem aims to determine the optimal arrangement of headways (i.e., time intervals between consecutive trips of the same bus line) for each line, in order to simultaneously maximize the number of successful transfers and minimize the cost of the system. Transfers are successful or effective only when two trips are synchronized, i.e., when a passenger has sufficient time to walk from the bus stop from which they alighted the first trip to the stop in which they are going to board the second trip. Additionally, the waiting time at the bus stop for the second trip must not exceed a certain threshold, ensuring a minimum quality of service (QoS) for passengers. Synchronizing transfers is also a very relevant issue in backbone-type networks for public transportation, such as the one proposed for Montevideo [24]. Driving travel times in a urban zone can be highly uncertain since they depend on several aspects (traffic, streets network conditions, weather, etc.). However, by analogy with similar works of the related literature [25; 26], the timetabling problem is addressed within a reference interval, during which relevant data for the problem--such as travel times between points and passenger volumes per unit of time--can be considered steady and of uniform distribution. This contributes to simplifying the complex timetabling problem and enables obtaining (near) optimal schedules for a specific period of interest, such as peak hours of the transport system. A particular feature of the model addressed in this work in comparison to others of the related literature is that the model does not assume a predetermined number of trips per line. Instead, the model considers that headways must fall within pre-established minimum and maximum values for each bus line. Additionally, the model guarantees a minimum number of trips per bus line to provide the quality of service for users who do not transfer.
## 4 Results and Discussion
### Particle Breakage Evolution
By combining the loading stress and loading velocity to explain their influences on the compressive deformation and particle breakage of the waste rock specimens, it can be deduced that both the axial strain and the particle breakage ratio have a logarithmic functional relationship with these two main control factors. The breakage degree of the waste rock filling materials is therefore closely linked to the compressive deformation under loading. The linear fitting equation for the axial strain and the breakage ratio is \(y=54.83x-0.45\). The correlation coefficient (\(R^{2}\)) is 0.9771, suggesting a highly linear relationship, as depicted in Figure 8.
## 3 Results and Discussions
### Analysis of the Influencing Factors of Compressive Strength  
Figure 5 shows that \(R_{p}\) of NCRPB with nine groups is 8.5-10.7 MPa, and the highest compressive strength of \(L_{4}\) is 10.7 MPa. The influencing factors were analyzed according to the extreme difference values of the compressive strength of each group, as shown in Figure 7. In Figure 7, the change in R/C had the most significant effect on the compressive strength of NCRPB, and the compressive strength significantly decreased with the increase in R/C. \(R_{p}\) of NCRPB decreased by 15.4% when R/C increased from 3.0 to 5.0. In addition, the variation in \(\mathrm{w_{f}}\), \(\mathrm{w_{l}}\) and W/C affected the compressive strength of NCRPB, but it was not as significant as that of W/C. The compressive strength of NCRPB slowly increased with increasing FAC and lime content. With increasing W/C (0.5-0.7), the compressive strength of NCRPB first increased and subsequently decreased. When W/C is less than 0.6, the saturation water consumption of the hydration reaction is not reached, so the reaction is not sufficient. After the saturation water consumption is exceeded, the compressive strength will significantly decrease if W/C is increased. Through the comparison of \(R_{p}\) values of each group, the best combination scheme of NCRPB is A2B1C3D3, but the mix ratio of this group does not appear in the orthogonal combination. In addition, considering that high lime dosing will reduce the utilization rate of rock powder and increase the cost, this paper selects A2B1C2D3 as the optimal group, and its compressive strength is 10.7 MPa, which is higher than the 10 MPa (MU10) specified in the Chinese wall brick specification, so this group has practical engineering significance. In addition, the extreme difference analysis of compressive strength (in Table 4) shows that the F values of the four influencing factors are 0.07, 3.43, 0.18 and 0.31, and only R/C has a greater F value than the critical value of extreme difference F\({}_{0.1}\) = 3.11 to reach the significance level, which further illustrates the significance of the influence of R/C.  
Figure 6: The failure mode of the NCRPB sample without FAC (**a**,**c**) and with FAC (**b**,**d**).
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 1 Introduction
Its architecture primarily comprises three components: an image encoder for calculating image embeddings, a hint encoder for embedding hints, and a mask decoder for predicting segmentation masks. Through a meticulously structured data engine, Meta Company has orchestrated the annotation process across three iterative phases: starting with assisted manual annotation, advancing to semi-automatic annotation, and culminating in fully automatic annotation. This process led to the creation of a large and diverse image segmentation dataset, encompassing natural scenes, urban environments, medical images, and satellite imagery. The dataset includes 11 million images and over 1 billion masks [24]. SAM's design, combined with this expansive dataset, endows it with potent zero-shot generalization capabilities. This implies that SAM can provide reliable predictions for images not encountered in the training set. The capability of SAM in the field of medical imaging and remote sensing hasalready been demonstrated [25, 26, 27, 28]. Moreover, recent studies have successfully applied SAM for parcel extraction [29, 30, 31]. In summary, parcel extraction using deep learning faces several significant challenges, including the need for large training datasets, poor generalization across different regions, sensors, and temporal conditions, and difficulties in adapting to the varied characteristics of farmland landscapes. To overcome these limitations, we proposed a novel approach that integrated SAM with multi-resolution remote sensing imagery to extract parcels. Our approach integrates SAM's zero-shot segmentation capabilities, a cropland mask, and overlapping prediction techniques. This eliminates the need for labor-intensive sample creation and enables fast, accurate parcel extraction across diverse temporal, geographical, and sensor contexts. In addition, considering the diversity of farmland landscapes, we have designed a technique that integrates images of multiple resolutions for parcel extraction. Through this technique, we apply the outcomes of parcel extraction from lower-resolution images to higher-resolution datasets, and employ a threshold-based decision process to determine the necessity for additional segmentation. This approach facilitates meticulous parcel extraction even within complex agricultural settings.
## 3 Design Rationale and System Architecture
### Decentralized Security Fabric  
As the right part of Figure 1 shows, the decentralized security fabric consists of two sub-systems: (i) a security services layer based on the microservice-oriented architecture (MoA); (ii) a fundamental security networking infrastructure atop the Blockchain and DDS. To address heterogeneity and efficiency challenges such as developing and deploying security services in the distributed network environment, our security services layer adopts container technology to implement microservices for PC applications. The key operations and security schemes are decoupled into multiple containerized microservices. As container is loss-coupled from the remaining system with the OS-level isolation, these microservices can be independently updated, executed, and terminated. Each microservice unit (or container) exposes a set of RESTful web service APIs to users of PC applications and utilizes local ABIs to interact with the SCs deployed on the Blockchain.  
The Blockchain network acts as a decentralized and trust-free platform for security services, and it uses a scalable PoW consensus protocol to ensure the immutability and integrity of the on-chain data on the distributed ledger if the majority (51%) of the miners are honest. The security mechanisms are implemented by self-executing SCs, which are deployed on the Blockchain by trusted oracles such as system administrators. Thus, the security service layer can provide secure and autonomous microservices in a decentralized manner. To reduce the overheads of directly recording large data on the distributed ledger, we bring DDS into the security infrastructure as off-chain storage, which is built on a Swarm [26] network. Unlike the IPFS, which does not guarantee storage, Swarm maintains content-addressed DHT and relies on data redundancy to offer secure and robust data services. Moreover, the inclusion of incentives makes Swarm more flexible to integrate with the Ethereum Blockchain. The meta-data of point clouds and operation logs that require a heterogeneous format and various sizes are encrypted and then saved into the DDS. Raw data on the DDS can be easily addressed by their references (Swarm hash), which are recorded on the Blockchain for audition and verification. A Swarm hash has a much smaller size (32 or 64 bytes) than its raw data; therefore, it is promising to improve efficiency in transaction propagation and privacy preservation without directly exposing raw data on the transparent Blockchain.
## 3 Methodology
### Defect Detection Framework  
The overall framework of the DPN-Detector proposed in this paper is shown in Figure 2. The Faster R-CNN is used as the baseline detector for defect detection on steel strip surfaces. The DPN-Detector consists of four parts: feature extraction, feature refinement, region proposal, and classification and regression. The steel strip images input to DPN-Detector will go through the above four parts in turn, and finally the predicted defect category and location are obtained.  
In the feature extraction part, the superior performance ResNet50 [35] is used as backbone to extract the features of the steel strip images. The parameters are initialized  
Figure 2: The overall framework of the proposed steel strip defect detection network (DPN-Detector). cls-pred: class prediction, bbox-reg: bounding-box regression. fc: fully connected layer, conv: convolutional layer.  
with the weights pre-trained in ImageNet, which can reduce the training time and make the overall model converge fast. In the feature refinement part, the features of different levels extracted by the backbone firstly pass the semantic information level by level through the top-down feature fusion path. The RASPP module is then used to enrich the multi-scale information of features and expand the receptive field. Finally, the bottom-up path is used to transfer the rich location information of the low-level features to the high-level features. In the region proposal part, the refined features of the DPN are fed into RPN to generate a large number of region proposals that may contain defects using sliding windows. In the classification and regression part, ROIAlign firstly takes the refined feature maps and regional proposals as input and maps the regional proposals to the corresponding features of different levels. Then, the features are intercepted in the region candidate boxes and resized into fixed-size ROI feature vectors. Finally, the double head [36] is used as the detection head to perform defect category classification and bounding box regression according to the ROI feature vectors.
## 4 Discussion
### Future Perspectives and Emerging Trends in CMSS  
Integrating new methodologies into the CMSS framework presents a significant opportunity for discovery and innovation. Key exploration areas include (i) the use of machine-learning algorithms for predictive analytics and decision making; (ii) the employment of Human-Computer Interaction (HCI) principles to develop intuitive interfaces and decision support tools; (iii) the application of blockchain technology to bolster data security, integrity, and transparency; and (iv) the integration of Multi-Agent Systems (MASs) for simulating complex interactions and coordination among autonomous agents. An illustrative example of a future research study inspired by the above could be the embedding of a chatbot into the CMSS, powered in the back end by a Large Language Model (LLM), to enhance the communication between the system and a participant actionable actor.