esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 5 Results  
### Test Bench Arrangement  
In this section, a description of the experimental test bench is reported. The host structure is a double-T profile beam (IPE120), which is simply supported at its ends (Figure 3a). Its geometrical and physical parameters are reported in Table 1.  
Three proof-mass actuators (Figure 3b) are placed on the beam according to the scheme depicted in Figure 4 (a detailed description of the modeling process of the actuator is reported in [5]). The position of the actuators has been chosen according to three anti-nodal points of the beam. Actuator 1 is placed on an anti-node of the third vibration mode of the beam, while Actuator 2 and 3 respectively on the anti-nodes of the second and fourth mode of the beam. Since the first vibration mode has a frequency lower than the actuators  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Length** & **Section Area** & **Linear Mass** & **Inertia** & **Young** \\  & & & & **Moment** & **Modulus** \\ \hline IPE120 & 4 [m] & 13.2 [cm\({}^{2}\)] & 10.4 [\(\frac{\text{Kg}}{\text{m}}\)] & 318 [cm\({}^{4}\)] & 206 [GPa] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Beam parameters.  
one, it cannot be controlled (due to the actuators dynamics). Due to the co-located control (sensor and actuator are embedded on the same device), the performance of the control system will depend on the position of the smart dampers on the structure. So, a correct installation of such devices is required. Three additional accelerometers are placed on the second-half of the beam with the same principle, in order to externally measure the performance of the control system. Sensor 0 is placed on the anti-node of the second mode and sensor 1 and 2 on the anti-nodes of the third and fourth mode respectively. In Table 2 the natural frequencies of the selected modes are reported, both for numerical simulations and experimental tests. The disturbance force is generated by another electrodynamic PMA placed on the structure. In this case, its position has been chosen properly in order to excite the second, third and fourth mode of the beam at the same time.  
Figure 4: Position of actuators and sensors on the test rig.  
Figure 3: Experimental test rig (**a**) and the stand-alone device (**b**).
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 6 Results and Discussion  
This section shows the simulation results that are conducted by using the described methods in the previous section. The numerical simulation results are carried out by the MATLAB/SimEvent software. The switch parameters are referenced from a full Ethernet industrial switch (RUGGEDCOM), as mentioned earlier in this paper. The obtained results comprise the performance of different switch architectures that are analyzed and discussed in this section. All architectures are implemented based on the traffic of 2000 packets per second, which complies with the GOOSE protocol requirements of 4 ms inter-packets time. Moreover, the rest of the parameters are set as follows: the service time is set to 20 ms and the buffer size set to 2 Mbits.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 2 Problem Formulation  
### Mathematic Model  
The mathematic model of range-based cooperative localization is described as follows. As is shown in Figure 1, consider a sensor network consisting of \(N\) source sensors, of which locations \(\mathbf{x}_{i}\in\mathbb{R}^{d}\), \(i=1,2,\ldots,N\) are unknown, and \(M\) anchor sensors of which the location \(\mathbf{x}_{k}\in\mathbb{R}^{d}\), k=N+1,...,N+M is known. \(d\) is the coordinate dimension. Source nodes are collected in the set \(\mathcal{S}=\{1,2,\ldots,N\}\), and anchors are collected in the \(\mathcal{A}=\{N+1,N+2,\ldots,N+M\}\) set. We denote the Euclidean distance between node \(i\) and \(j\) as \(d_{i,j}\), and the noisy range measurement as \(r_{i,j}\). In general, not every pair of nodes can communicate because the communication distance has an upper limit. We denoted this distance limit as \(r_{\mu}\). The neighbor of node \(i\) is denoted as \(j\in\mathcal{N}_{i}\) if \(r_{i,j}\) is available, i.e., \(\mathcal{N}_{i}=\{j|r_{i,j}\leq r_{\mu},\forall j\in\mathcal{S}\},\ \forall i\in \mathcal{S}\cup\mathcal{A}\). We denoted the pairwise of source sensors as \((i,j)\in\mathcal{Z}_{\mathcal{S}}\), and the pairwise between a source sensor and an anchor as \((i,k)\in\mathcal{Z}_{\mathcal{A}}\). The distance between two nodes is defined as  
\[d_{i,j}=\|\mathbf{x}_{i}-\mathbf{x}_{j}\| \tag{1}\]  
Considering the impact of NLOS propagation on distance estimation, we divided the range measurements into two sets. We used \(\mathcal{Z}_{\text{LOS}}\) (and, respectively, \(\mathcal{Z}_{\text{NLOS}}\)) to denote the set of pairwise nodes in which connections between nodes are LOS (and, respectively, NLOS). Hence, the range measurements are defined as:  
\[\begin{cases}r_{i,j}=d_{i,j}+n_{i,j},(i,j)\in\mathcal{Z}_{\text{ LOS}}\\ r_{i,j}=d_{i,j}+\epsilon_{i,j}+n_{i,j},(i,j)\in\mathcal{Z}_{\text{NLOS}}\end{cases} \tag{2}\]  
where \(n_{i,j}\sim N(0,\sigma_{i,j}^{2})\) is the measurement noise following a zero-mean Gaussian distribution with variance \(\sigma_{i,j}^{2}\), and \(\epsilon_{i,j}\) is the error of the NLOS measurement that is exponentially distributed with a mean parameter \(\alpha_{i,j}=\alpha_{\text{NLOS}}\). The value of \(\alpha_{\text{NLOS}}\) depends on the NLOS propagation environment [40; 41; 42].  
In most cases, prior information of NLOS connections, such as NLOS distribution, is unavailable. In this paper, our model was designed for such tough scenarios, i.e., NLOS connections being  
Figure 1: Wireless sensor network (WSN) figuration. Blue circles denote anchors; white circles denote source nodes. The physical model of non-line-of-sight (NLOS) connections is presented by _obstacle_ and _scattering object_.  
unidentifiable among all connections. First, since we could not distinguish which connections were NLOS, we assumed that all range measurements were NLOS. Model \(r_{i,j}\) was modified as  
\[r_{i,j}=d_{i,j}+\varepsilon_{i,j}+n_{i,j},(i,j)\in\mathcal{Z}_{\text{NLOS}} \cup\mathcal{Z}_{\text{LOS}} \tag{3}\]
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### _Socce Layer_  
The Score Layer is responsible for quantifying the semantic relevance between event descriptions generated by the Large Language Model (LLM) and a set of predefined anomaly labels. To achieve this, we adopt a Sentence Transformers architecture, which processes inputs through the following three stages:  
**Sentence Embedding Generation**: Event descriptions from the LLM (e.g., \"object left in restricted area\") are first encoded into context-aware word vectors through stacked Transformer layers (Figure 3), then compressed into fixed-dimensional sentence embeddings via mean pooling. This process captures the semantic nuances of textual event descriptions.  
**Offline Label Encoding**: Predefined anomaly labels (e.g., \"intrusion\", \"fire smoke\") are concurrently processed using the same Transformer architecture to generate label embeddings. These embeddings are pre-computed offline to ensure real-time performance during inference.  
**Real-Time Similarity Scoring**: The system computes cosine similarity scores between the dynamically generated sentence embeddings and pre-encoded label embeddings in real time. Higher similarity scores indicate stronger semantic alignment between events and anomaly categories, enabling the system to filter events exceeding confidence thresholds and prioritize responses based on score ranking.  
The complete workflow is illustrated in Figure 6, which highlights the interaction between these three stages through a clear data flow diagram [44].
## 4 Results and Discussion
### Baseline Performance Analysis and Bottleneck Identification (Pre-Optimization)
#### 4.2.4 Cycle Time and Throughput Analysis  
The cycle time and throughput revealed significant performance disparities between stations (Figure 9):  
Figure 7: Process discovery model showing transitions between stations. Red boxes highlight bottlenecks; blue boxes and arrows indicate normal flow; green arrows mark efficient transitions; red arrows show problematic transitions; gray arrows represent rework. Edge labels show transition counts.  
* Cycle Time Range: 9.4 to 16.0 min per event
* Best Performer: Station 207 (9.4 min/event)
* Worst Performer: Station 209 (16.0 min/event)
* System Average: 11.7 min/event The throughput analysis showed:
* Highest Throughput: Station 207 (159 events/day)
* Lowest Throughput: Station 209 (94 events/day)
* Average Throughput: 126.3 events/day
* System Efficiency: 87% In particular, bottleneck stations (202-205) demonstrated moderate cycle times (10.6-13.6 min/event) but maintained relatively consistent throughput rates (108-137 events/day), suggesting that their bottleneck status is primarily due to high processing volumes rather than inefficient operations.  
Figure 8: Duration by activities showing operational hours per station. Red bars indicate bottleneck stations, while blue bars represent normal operations. The clear distinction demonstrates the concentration of processing time at bottleneck stations.  
Figure 9: Cycle time and throughput analysis. The left panel shows cycle time by station with purple bars, while the right panel displays throughput rates with green bars. Key metrics are summarized below, indicating an overall system efficiency of 87%.
## 5 Conclusions  
Compared to traditional nodes in WSNs, today's sensor nodes have more powerful resources, so the nodes are able to carry out more complicated operations and more diverse applications can be applied to WSNs. Among the applications, many applications deal with audio data because audio data are among the most widely used data types, and sensor nodes are able to utilize microphones easily. However, most applications require large computations and processing to be completed within a tight deadline, so it is difficult for a single node to run relatively heavy applications by itself. To overcome this limitation, in this paper, we proposed the cooperative computing system, _HeaLow_, for heavy-computation and low-latency processing in WSNs. Unlike related studies which evaluate their own systems through simulations only, we implemented _HeaLow_ on real devices. Thus, we conducted experiments using real devices as well as simulations, and the results showed that nodes using _HeaLow_ in WSNs are able to run applications which require heavy operations with low-latency. We consider processing acoustic signals, which can be used for various services and applications in WSNs, as the target task in this paper. Thus, such services and applications are able to utilize _HeaLow_ easily. However, since _HeaLow_ was not designed to be specialized in specific applications, _HeaLow_ can be used for any application.  
We have several directions for future work. Since _HeaLow_ attempts to offload data when the recipient confirms the request, the duty cycling of WSNs is not a problem to run our system. However, the performance of the proposed system might be degraded due to neighbors in sleep state. Thus, we will analyze the performance of our system applied to WSNs with duty cycling.  
Figure 7: Localization error varying on the amount of workloads.  
In addition, we plan to theoretically analyze whether _HeaLow_ can be generalized to deal with representative task assignment problems. In addition, we would like to implement the proposed system on more various platforms and evaluate the system in a variety of situations.  
H.K. governed the overall procedure of this research. [PERSON] designed and implemented _HeaLow_. [PERSON] devised the offloading decision process of _HeaLow_. [PERSON] and [PERSON] designed and performed the experiments and the simulations, and wrote the paper.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059151), and in part by \"Human Resources program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20174030201820).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless Sensor Network \\ IoT & Internet of Things \\ MEMS & Micro Electro Mechanical Systems \\ FFT & Fast Fourier Transform \\ \end{tabular}
## 3 Congestion-Aware EFL-Based Model Aggregation Policy in Edge Computing
### System Architecture
#### 3.1.1 Participant Tier  
In the proposed EFL framework, participants are expected to fulfill three fundamental requirements, outlined as follows:  
1. **Adequate local resources**: Participants need to ensure the connectivity, energy capacities, and model computing capabilities within both auxiliary and intrinsic memories.
2. **High-quality data input**: The integrity and reliability of the data are required in contributing to the overall performance of our framework.
3. **Non-malicious intent**: This requirement ensures the security and trustworthiness of the collaborative learning environment for all selected participants.  
Meeting these prerequisites grants participants the authorization to engage in collaborative training and be considered for selection when scheduling interactions with the global server. Let \(N=\{1,2,\ldots,n\}\) denote the set of selected IIoT participants. As each participant is associated with a specific image processing service, we collect the corresponding featuresof \(m\) services between the participant and aggregator by denoting \(Y_{n}^{m}=\left\{Y_{n}^{1},Y_{n}^{2},\ldots,Y_{n}^{m}\right\}\) to participant \(n\). \(M_{n(t)}\) is based on individual data contributions \(D_{n(t)}\) and linked to a unique service type identified as \(Y_{n}^{m}\).
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
###### Abstract  
In this paper, a patient mobility support scheme for indoor non-directed optical body area networks (OBAN) is presented. The OBAN is an optical healthcare system where medical sensors are installed on various parts of the patient's body and are connected to an optical coordinator for transmitting the physiological signals via optical wireless links. In the proposed scheme, a white light-emitting diode (LED) was employed as the optical coordinator that was mounted on the patient body, while a photodetector (PD) was used as the receiver installed at the ceiling. We considered three practical mobility scenarios in terms of the location of the coordinator: (i) Shoulder, (ii) wrist, and (iii) both shoulder and wrist. The analytical channel model for multiple reflections in a non-directed OBAN was developed and validated in the form of simulations. In addition, experiments were carried out to verify the effectiveness of the proposed mobility scheme. It was found that the third scenario (shoulder and wrist) performed best, showing a bit error rate (BER) of 1.2 \(\times\) 10\({}^{-6}\) at a distance of 1.25 m. The experimental results demonstrated that the proposed mobility support scheme in the OBAN added an additional degree of freedom to patients with reliable performances.  
optical communications; light-emitting diodes; mobility; diversity Article
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 7 Performance Analysis
### Time Cost of Search Algorithm  
When the user initiates a query operation, a token for the corresponding keyword is generated, and the token is then sent to the server. Figure 8 shows the relationship between the number of keyword document pairs and the search time when the server performs a search. In the experiment, the three schemes Chen1 [24], Liu [25], and Chen2 [20] were compared. In order to obtain a more fair result, the experiment added up the search token generation time of each scheme for comparison, equivalent to calculating the total process of the data user to obtain the encrypted file of the file.  
Figure 6: Impact of different hashing times on search time (add).  
Figure 7: Impact of different hashing times on search time (del).  
As we can see from Figure 8, the search time in this article is less than in other scenarios, whether the keyword is present or not. If it does not exist, simply return and prompt. As can be seen from the Figure 8, the search time without keywords is between 0.03 ms and 0.05 ms, which can greatly improve the search rate and reduce the waiting time for users to receive feedback. Compared with other experiments, it is necessary to conduct a chain search for all keywords and then give feedback.  
For the search of non-existent keywords, since the other three experiments are all using the same chain storage mode, they are combined into one for comparison (shown in Figure 9). It can be seen that once the keywords exceed 100,000, the chain search method will gradually increase the time. However, the time consumed in the search method in this paper is basically stable, and the time consumed is not increased due to the growth of the total number of searches. The data will only fluctuate in a small range, and the feedback time of users will be greatly shortened.  
Figure 8: Time cost of search algorithm [20; 24; 25].  
Figure 9: Time spent searching for a keyword when the keyword does not exist.
## 4 Experiments
### Evaluation Indicator  
The evaluation indicators of subject-sensitive hashing mainly include: robustness, tampering sensitivity, security, computational performance, and digestibility.  
1. Robustness. For a single HRRS image, robustness means that, after the image undergoes an operation, the hash sequence does not change, or the change is lower than a preset threshold. Due to the strong chance of a single or a small number of test data, we measured the robustness using the proportion of HRRS images whose hash sequence variations were lower than the preset threshold \(T\). The calculation method was as follows: \[R(T)=\frac{Num_{R}}{Num_{T}}\] (3)where \(Num_{T}\) represents the number of instances participating in the test and \(Num_{R}\) represents the number of instances whose hash sequence has not changed or has changed below the threshold \(T\).
2. Tampering sensitivity. Like cryptographic hash and fragile watermarking, a subject-sensitive hash algorithm has to detect whether the image content has been tampered with, which means that tampering sensitivity is an important evaluation indicator for subject-sensitive hashing. For a single instance of image tampering, the hash sequences before and after the image is tampered with should change by a greater magnitude than the threshold \(T\). Similar to the robustness test, tampering sensitivity also requires more test instances to be more convincing. We used the proportion of instances in which tampering was detected to describe tampering sensitivity, as follows: \[S(T)=\frac{Num_{S}}{Num_{T}}\] (4) where \(Num_{T}\) represents the number of instances participating in the test and \(Num_{S}\) represents the number of tampering instances that were successfully detected.
3. Digestibility. The storage space occupied by the hash sequence should be as small as possible--that is, the hash sequence should be as short as possible.
4. Computational performance. Computational performance requires that the time to calculate and compare hash sequences should be as short as possible. In fact, as the comparison of hash sequences is very efficient due to digestibility, computational performance generally focuses on the efficiency of generating hash sequences.
5. Security. The security of subject-sensitive hashing means that the content of the image cannot be obtained from the subject-sensitive hash sequence.
## 6 Analysis of Results
### Problem 2  
The problem of minimizing the life cycle cost of an arrangement of sensors subject to precision and availability constraints on a set of key flowrate estimates is posed as follows  
\[\begin{array}{ll}Min&LCC(\mathbf{q})\\ s.t.&\\ &\hat{\sigma}_{j}(\mathbf{q})\leq\sigma_{j}^{*}&\forall\ j\in S_{\sigma}\\ &A_{k}(\mathbf{q})>A_{k}^{*}&\forall\ k\in S_{{}_{A}}\\ &\mathbf{q}\in\{0,1\}^{\alpha}_{j}\\ \end{array} \tag{13}\]  
\begin{table}
\begin{tabular}{|c|c|} \hline Case & Constraints \\ \hline
1 & E\(\geq\) 1 for stream 1 \\  & \(\sigma_{2}^{*}\)=0.025 \(\sigma_{6}^{*}\)=1.785 \\ \hline
2 & E\(\geq\) 1 for stream 2 \\  & \(\sigma_{10}^{*}\)=1.048 \(\sigma_{28}^{*}\)=1.445 \\ \hline
3 & E\(\geq\) 1 for streams 17 23 \\  & \(\sigma_{4}^{*}\)=2.199 \(\sigma_{8}^{*}\)=3.281 \(\sigma_{21}^{*}\)=1.754 \(\sigma_{25}^{*}\)=1.709 \\ \hline
4 & E\(\geq\) 1 for streams 7 16 18 20 \\  & \(\sigma_{4}^{*}\)=2.199 \(\sigma_{5}^{*}\)=1.065 \(\sigma_{8}^{*}\)=3.281 \(\sigma_{12}^{*}\)=1.345 \(\sigma_{27}^{*}\)=1.415 \(\sigma_{28}^{*}\)=1.445 \\ \hline
5 & E\(\geq\) 1 for streams 5 12 14 35 37 44 62 70 77 \\  & \(\sigma_{10}^{*}\)=1584.2, \(\sigma_{17}^{*}\)=1359.6, \(\sigma_{35}^{*}\)=200.7, \(\sigma_{39}^{*}\)=1580.6, \(\sigma_{56}^{*}\)=122.72, \(\sigma_{69}^{*}\)=1284.4 \\ \hline
6 & \(A_{4}^{*}\)=0.9, \(A_{8}^{*}\)=0.9 \\  & \(\sigma_{3}^{*}\)=0.7, \(\sigma_{8}^{*}\)=0.5 \\ \hline
7 & \(A_{4}^{*}\)=0.8, \(A_{8}^{*}\)=0.8, \(A_{10}^{*}\)=0.8 \\  & \(\sigma_{3}^{*}\)=0.7, \(\sigma_{8}^{*}\)=0.15 \(\sigma_{12}^{*}\)=0.4 \\where \(LCC\) indicates the life cycle cost of the sensor structure, \(A_{k}\) stands for the availability of the \(k\)-th variable estimate and, \(S_{\sigma}\) and \(S_{A}\) are the set of variables subject to precision and availability constraints on their estimates respectively. The formulations used for \(LCC\) and \(A_{k}\) in this work are the same presented by [PERSON] et al. [16]. Variable availabilities are calculated using the cutsets of the process graph and sensor failure probabilities. The evaluation of availability constraints incorporates additional complexity to Problem 2. Two case studies are presented for this formulation that differ in the number of problem constraints.  
Cases 6 and 7  
The flowmeter network design for a simplified hydrodealkylation plant, constituted by 8 nodes and 14 edges, is conducted by solving the optimization problem represented by equation (13). The search space is formed by \(2^{14}\) solutions. Process flowsheet, purchase cost and precision data for the set of available sensors are extracted from the work by [PERSON] and [PERSON] [5]. Problem constraints are included in Table 1.  
Also Cases 6 and 7 were previously solved using the SO-TS procedure [16] which systematically outperforms C-TS and HGA.