esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The most common techniques are one parameter at a time (OPAT), Fourier amplitude sensitivity test (FAST) regression-based methods and variance-based methods [56; 58; 59]. These methods, however, are often computationally expensive or unreliable for large numbers of agents or they require excessive numerical simulation times [56; 58; 59; 60; 61]. Alternative schemes for sensitivity analysis are therefore worth exploring for running large-scale ABM with long-time scale simulations. For instance, Random forests, a popular machine learning technique, has been applied for the quantification of parameter importance and parameter selection [62; 63; 64]. Random forest was proposed to handle high dimensional data, thousands of input variables, complex parameter interaction, and missing data in complex models [65]. The primary goal of this study was to improve the accuracy of existing VF-ABM in simulating cellular and molecular activities associated with surgical vocal fold injury and repair. Experimental and computational studies were conducted. This paper was structured to present: (1) The experimental study of identifying and phenotyping immune and repair cells in surgically injured rat vocal folds up to four weeks after surgical injury; as well as (2) the computational study of optimizing the biological representation of VF-ABM through sensitivity analysis, model calibration and verification using flow cytometry data.
## 3 Experiments and Discussions
### System Parameters
Therefore, the axial resolution corresponds to the width of the PSF at 6 dB below the peak (1005, 133) in Figure 9. The measured axial resolution is 4.8 \(\upmu\)m. The theoretical axial resolution calculated by Equation (14) is 4.3 \(\upmu\)m (The center wavelength \(\lambda_{0}\) of SLD is 850 nm and the bandwidth \(\Delta\lambda\) is 75 nm). Therefore, the measured axial resolution is very close to the theoretical axial resolution. \[\delta z=\frac{2 ln2}{\pi}\times\frac{\lambda_{0}^{2}}{\Delta\lambda}=\frac{2 ln2}{\pi}\times\frac{850^{2}}{75}=4.3\ \upmu\text{m} \tag{14}\]  
Subsequently, the lateral resolution calculated from Equation (15) is 13.53 \(\upmu\)m (the focal length \(f_{Obj}\) of object lens is 75 mm, the spot diameter \(d\) of parallel light is 6 mm). \[\delta x=\frac{4\lambda_{0}f_{Obj}}{\pi d}=\frac{4\times 850\times 75}{\pi \times 6}=13.53\ \upmu\text{m} \tag{15}\]  
Finally, the image depth of homemade SD-OCT system can be defined as the location where the intensity of PSF drops into half (\(-6\) dB). As shown in Figure 10, the imaging depth of the system in air is 1.512 mm. Therefore, the full-range SD-OCT system's image depth in air is 3.024 mm. Figure 10: PSF of homemade SD-OCT system at different depths. Figure 9: The point spread function (PSF) of SD-OCT system.
## 4 Hardware Design
### Data Concentrator  
The key components of the data concentrator are similar to those of the data collector. These devices can share most of the drivers and application codes, which greatly reduces the difficulty of our design and of debugging. However, the data concentrator requires more external memory because it is responsible for transmitting electricity meter data from hundreds of data collectors to the remote centralized server. A GPRS module is also added to the design of the data concentrator. Modularity is a key tenet of our design, reflected in the separation of the GPRS module and the 470 MHz wireless module on the data concentrator. The basic hardware structure is illustrated in Figure 3.  
_Memory_: We use the AT45 DB642D instead of the AT45 DB041D. Its 69,206,016 bits of memory are organized as 8,192 pages. In addition to the main memory, the AT45 DB642D also contains two SRAM buffers of 1,056 bytes each.  
_GPRS module_: We chose the SIM300C for the data concentrator. The SIM300C is a tri-band GSM/GPRS engine that works in the EGSM 900 MHz, DCS 1,800 MHz, and PCS 1,900 MHz frequencies. With a tiny configuration of 50 \(\times\) 33 \(\times\) 6.2 mm, SIM300C meets the space requirements in our industrial application. The SIM300C is designed to have power-saving features: the current consumption is as low as 2.5 mA in sleep mode.
## 5 Conclusions  
In this work, a simple method to estimate the sag value by measuring the conductor slope at the span extremities was derived. The proposed solution can be adopted for both cases of span with supports at equal or different heights. The main advantage of the method is that the parameters needed for the sag estimation are geometrical quantities easily measurable on an operative line. The angle measurement is performed by means of wireless sensors developed by the authors and adopted in the field for the monitoring of wind-induced vibrations occurring on HVTL conductors. These sensors are endowed with a MEMS accelerometer, which is used, in this case, essentially as an inclinometer. A laboratory test was arranged to demonstrate the ease of implementing the proposed solution. The test on a real cable allowed to check the efficiency of the method and the monitoring system in obtaining a sag estimation from angular measurements for different tensile loads imposed to the cable. Through data analysis and comparison with reference values, it was then demonstrated that the solution proposed is suitable to the scope, and represents a low-cost, easy to install, and robust system to perform a complete set of monitoring activities in the field
Sensors_**2014**, _14_, 17451-17470; doi:10.3390/s140917451  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Active Disaster Response System for a Smart Building  
[PERSON] **1**, [PERSON][PERSON] **1**,**\(\star\)**, [PERSON] **2** and [PERSON] **2**  
* Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan; E-Mail: [EMAIL_ADDRESS]
* Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan; E-Mails: [EMAIL_ADDRESS] (L.-W.K.); [EMAIL_ADDRESS] (J.W.S.L.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-05-534-2601 (ext. 4519); Fax: +886-05-531-2170.  
_Received: 20 March 2014; in revised form: 11 September 2014 / Accepted: 11 September 2014 / Published: 18 September 2014_  
**Abstract:** Disaster warning and surveillance systems have been widely applied to help the public be aware of an emergency. However, existing warning systems are unable to cooperate with household appliances or embedded controllers; that is, they cannot provide enough time for preparedness and evacuation, especially for disasters like earthquakes. In addition, the existing warning and surveillance systems are not responsible for collecting sufficient information inside a building for relief workers to conduct a proper rescue action after a disaster happens. In this paper, we describe the design and implementation of a proof of concept prototype, named the active disaster response system (ADRS), which automatically performs emergency tasks when an earthquake happens. ADRS can interpret Common Alerting Protocol (CAP) messages, published by an official agency, and actuate embedded controllers to perform emergency tasks to respond to the alerts. Examples of emergency tasks include opening doors and windows and cutting off power lines and gas valves. In addition, ADRS can maintain a temporary network by utilizing the embedded controllers; hence, victims trapped inside a building are still able to post emergency messages if the original network is disconnected. We conducted a field trial to evaluate the effectiveness of ADRS after an earthquake happened. Our results show that compared to manually operating emergency tasks, ADRS can reduce the operation time by up to 15 s, which is long enough for people to get under sturdy furniture, or to evacuate from the third floor to the first floor, or to run more than 100 m.  
**Keywords:** intelligent system; disaster preparedness; smart environment; automatic response system
###### Abstract  
Convex 1-D first-order total variation (TV) denoising is an effective method for eliminating signal noise, which can be defined as convex optimization consisting of a quadratic data fidelity term and a non-convex regularization term. It not only ensures strict convex for optimization problems, but also improves the sparseness of the total variation term by introducing the non-convex penalty function. The convex 1-D first-order total variation denoising method has greater superiority in recovering signals with flat regions. However, it often produces undesirable staircase artifacts. Moreover, actual denoising efficacy largely depends on the selection of the regularization parameter, which is utilized to adjust the weights between the fidelity term and total variation term. Using this, algorithms based on second-order total variation regularization and regularization parameter optimization selection are proposed in this paper. The parameter selection index is determined by the permutation entropy and cross-correlation coefficient to avoid the interference by human experience. This yields the convex 1-D second-order total variation denoising method based on the non-convex framework. Comparing with traditional wavelet denoising and first-order total variation denoising, the validity of the proposed method is verified by analyzing the numerical simulation signal and the vibration signal of fault bearing in practice.  
second-order total variation denoising; convex optimization; non-convex regularization term; permutation entropy; fault diagnosis Article
## 3 Methodology
### Model Architecture
#### 3.2.2 Model Architecture  
Our binary classification model comprises a sequential arrangement of layers, starting with an LSTM layer featuring 30 units. This LSTM layer is followed by a dropout layer, which introduces regularization during training by randomly deactivating neurons, thus reducing the risk of overfitting and promoting generalization.  
Subsequently, we incorporate a dense layer with a SoftMax activation function at the end. The dense layer generates a probability distribution over the two classes, namely, \"BENIGN\" and \"attack,\" allowing our model to provide confident predictions.  
The LSTM-based IDS model is optimized using the Adam optimizer and trained with the sparse categorical cross-entropy loss function, suitable for multi-class problems with integer labels.  
To illustrate the architectural flow, we present Figure 2 which depicts the schematic representation of our LSTM-based IDS model. This figure visually portrays the architectural data flow of our binary classification model through the LSTM layer, followed by the dropout and dense layers, signifying the sequence of computations performed by our model.  
Input  
Figure 2: Schematic representation of the LSTM-based IDS model.  
By harnessing the expressive power of LSTM networks, our model effectively captures the temporal dynamics inherent in network traffic data, facilitating accurate and timely detection of potential cyber threats within IoT networks. The LSTM's unique capacity to retain long-term dependencies, combined with its sequential data processing capabilities, empowers our IDS to achieve superior performance, reinforcing the resilience of IoT environments against intrusion attempts and bolstering overall cybersecurity measures.
## 4 Numerical Results and Discussions
### Performance Analysis with Comparative Schemes
The proposed JPCRA scheme outperforms the other three schemes, and the smaller \(\sigma_{u}^{2}\) is, the greater the performance gap, while a smaller \(\sigma_{u}^{2}\) results in a greater impact of resource allocation. Considering the influence of \(\sigma_{u}^{2}\) on system performance, the primary user with less influence on the secondary network interference should be selected for spectrum sharing when selecting spectrum sharing objects.
## Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks
Firstly, the frequently requested content can be obtained through the closely located cache resources. Secondly, caching mitigates the network congestion by eliminating redundant data transmission. This can substantially alleviate the data traffic burden from the backhaul link. Thus, the data caching can be performed during off-peak hours and the UE can fulfill its data demands with less delay during peak hours. This approach reduces network delay by up to 35% because data is accessed through the closely located cache resources rather than the core network [17; 18]. In one of the preliminary works [19], the concept of femtocaching is proposed, where data placement at a SBS is performed to minimize network delay. In a realistic urban scenario, UE is densely available. The UE's density can be exploited to enlarge the amount of cached content and boost the data sharing among proximal sets of UE through D2D communications. Hence, two-tier data caching at the SBS and UE levels appears to be a promising approach to significantly reduce network delay. Moreover, UE is mobile and its position evolves with respect to time. Therefore, UE exhibits intermittent connectivity with the data source. This poses a significant challenge to deliver content within a limited period of time. The UE's transitions from one location to another location are modeled through Markov chain, and the UE's mobility pattern is random, therefore its connectivity follows Poisson distribution [20; 21]. However, the UE's mobility can be utilized as an advantage because its mobility can increase the likelihood of discovering desired content within the D2D communication range. This rise in the likelihood is due to the fact that as the UE's connectivity with other UE varies, there is an increase in the probability of encountering UE containing the desired content. Hence, the inclusion of the UE's mobility features, such as the sojourn time and the UE's contact and inter-contact intervals, is imperative in designing an optimal caching mechanism.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 4 DC Microgrid SE and System Configuration  
### SE for DC System  
Since the proposed state estimator is intended for DC microgrid application, the state variable that would be used is the voltage magnitude \(V_{N}\). The resulting vector \(x_{DC}\) contains the states of all buses, which are the voltage magnitude of each bus inside the DC system, denoted as  
\[x_{DC}=\left[\begin{array}{c}V_{1}\\ \vdots\\ V_{N}\end{array}\right], \tag{14}\]  
with \(N\) as the number of buses.  
The measurements that could be considered for a DC microgrid SE are the voltage magnitude measurement, the current flow measurement, the power flow measurement, the current injection measurement, and the power injection measurement [15]. For the current and power flow measurements, the equations are written as  
\[I_{flow,ij}=\big{(}V_{i}-V_{j}\big{)}\cdot g_{ij} \tag{15}\]  
and  
\[P_{flow,ij}=V_{i}\cdot\big{(}V_{i}-V_{j}\big{)}\cdot g_{ij}, \tag{16}\]  
where \(i\) is the sending bus, \(j\) is the receiving bus, and \(g_{ij}\) is the conductance of the line between the buses. For the current and power injection measurements on bus \(i\), the equations are written as  
\[I_{inj,i}=\sum_{j=1}^{N}\big{(}V_{i}-V_{j}\big{)}\cdot g_{ij} \tag{17}\]  
and  
\[P_{inj,i}=\sum_{j=1}^{N}V_{i}\cdot\big{(}V_{i}-V_{j}\big{)}\cdot g_{ij}. \tag{18}\]
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 6 Conclusions  
The work presented here shows the scope, the methods and the results of applied research to develop a monitoring and tracking device for dry containers. This task is part of the so-called 5 GT system employing a novel combination of telecommunication technologies, notably 5G, Satcom, NB-IoT and BLE mesh, to realize a ubiquitous and massive real-time monitoring service of intermodal containers. Starting from a preliminary phase, in which the state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, the following sections showed how a prototype version of the Container Tracking Device (CTD) has been designed, developed and verified as the first fundamental step. This enabled the subsequent industrial realization of the devices used in the final assessment of the 5G SENSOR@SEA [1] project in an operational environment, which will be presented in a future article currently in preparation.  
The tests performed showed that the firmware is capable of handling the various functions of the device. Furthermore, by measuring the power consumption measurements of the CTD in different operating modes, it was possible to suggest the size of the battery pack. Different operating modes of the device were tested, showing battery needs that can be assumed as the baseline for future improvements.  
Future works can be addressed to further optimize the power consumption. Energy savings can be achieved by optimizing software, especially in the management of radio transmissions and by introducing power systems with rechargeable batteries.  
More generally, in the subject of \"smart container\" systems, important developments are expected to be achieved with the integration of low earth orbit satellite communication technologies and 6G cellular networks for the massive development of the Internet of Things.  
Conceptualization, M.F., M., S.N. and P.P.; methodology, M.F., M., S.N. and P.P.; software, M. and M.F.; validation, M.; formal analysis, M.F.; investigation, M.F., M. and S.N.; resources, M.F., M., S.N. and P.P.; data curation, M.F. and M.; writing--original draft preparation, M.F.; writing--review and editing, M.F., M., S.N. and P.P.; visualization, M.F.; supervision, M.F.; project administration, S.N.; funding acquisition, S.N. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the European Space Agency (ESA) through the 5G SENSOR@SEA project (contract no. AO/1-10285/20/NL/AF). The 5G SENSOR@SEA project was carried out under the ARTES 5G/6G Strategic Programme Line and funded by ESA.
# Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP
## 2 Methodology
### The ACO Algorithm  
This section briefly presents the ACO metaheuristic, which belongs to a bio-inspired class of optimization algorithms. The ACO algorithm proposed by [PERSON] [39] is inspired by real ants' behavior in nature. Ants' communication takes place through the secretion of a semiochemical and odorous substance called a pheromone to guide the direct food path from the destination to the source [40]. To search for food sources, ants go out and realize a path, dropping a certain amount of pheromones on the ground. Pheromone initialization is the most critical step, and the initialized quantity of pheromones is equivalent for all graph nodes [40]. The edge pheromone amount, \(\tau_{ij}\), between edges \(i\) and \(j\) is evaluated according to the following formula:  
\[\tau_{ij}=(1-\rho)\tau_{ij}+\sum_{k=1}^{n_{\text{a}}}\Delta\tau_{ij}^{k} \tag{7}\]  
where \(\rho\) is the pheromone evaporation rate, \(n_{\text{a}}\) is the number of ants that visited the edge \((i,j)\), and \(\Delta\tau_{ij}^{k}\) is the amount of pheromone deposited by ant \(k\) in the edge \((i,j)\). This amount of pheromone is calculated with:  
\[\Delta_{ij}^{k}=\left\{\begin{array}{ll}Q/L_{k},&\text{if the ant $k$ has passed through this edge}\\ 0,&\text{otherwise}\end{array}\right. \tag{8}\]  
where \(L_{k}\) measures the path distance visited by ant \(k\), and \(Q\) is a constant value. After the ants have passed a particular edge of the graph, they need to choose the next edge to follow in order to reach the food source. The amount of pheromone in the next edge to choose will be decisive, so the greater this amount is, the greater the probability will be that the ant will select the corresponding edge. The probability of movement of ant \(k\) from node \(i\) to node \(j\), which has not been visited yet, is calculated with:  
\[p_{ij}^{k}=\frac{[\tau_{ij}]^{a}[\eta_{ij}]^{\beta}}{\sum_{l\in N_{i}^{k}}[ \tau_{ij}]^{a}[\eta_{ij}]^{\beta}} \tag{9}\]  
where \(\alpha\) is a parameter that represents the importance of the edge pheromone in deciding the subsequent movement of the ant; \(\beta\) is a parameter that represents the importance of the distance in deciding the subsequent movement of the ant; \(N_{i}^{k}\) is a set of nodes that have not yet been visited by ant \(k\); \(\eta_{ij}\) is the inverse distance between nodes \(i\) and \(j\). A cycle is completed when all ants have finished their movement. As time goes by, it turns out that ants will tend to follow the shortest path, thus tending to converge to the problem's optimal solution.  
The ACO algorithm can be described step by step, as presented in Algorithm 1.  
```
1:Initialize parameters
2:Position ants at starting node
3:while stop condition is not satisfied do
4: Ants build a solution
5: Update pheromone
6: Local search (optional)
7: Update best solution
8:endwhile
9: Save best solution
```  
**Algorithm 1** ACO Algorithm.
# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 4 Experimental Results and Discussions
### Specific Feature Contributions by Target Class in the Hospital Mobility Model
For the HIGH target class, which considers patients with expected high hospital mobility, it is observed that certain features, when modified, have an effect that reduces the prediction probability from 0.13 to 0. In other words, a decrease in hospital staff (HOSPITAL STAFF) with a SHAP value of 0.01 (VALUE of \(-\)0.54), a reduction in the number of departments (DEPARTMENTS) with a SHAP value of 0.01 (VALUE of \(-\)0.68), and a decrease in the number of physicians (PHYSICIANS) with a SHAP value of 0.03 (VALUE of \(-\)0.34), are associated with a lower probability of patients being classified as high mobility. Similarly, an increase in interventions (INTERVENTIONS) with a SHAP value of 0.03 (VALUE of 0.02) and an increase in readmissions (READMISSIONS) with a SHAP value of 0.01 (VALUE of 0.19) also contribute to this decrease. These changes reflect how the model predicts that modifications to these specific characteristics reduce the probability that a patient will travel significant distances to receive care, potentially preferring closer options.
# Learning Translation-Based Knowledge Graph Embeddings by \(N\)-Pair Translation Loss
## 4 Considering Multiple Negative Triples though \(N\)-Pair Translation Loss
From \(\mathcal{G}\) and \(\mathcal{G}^{\prime N}_{(h,r,t)}\), the vector representations of entities and relations are found by minimizing the following _N_-pair translation risk over \(\mathcal{G}\). \[\mathcal{R}(\mathcal{G})=\frac{1}{|\mathcal{G}|}\sum_{(h,r,t)\in\mathcal{G}} \mathcal{L}_{npair}(\{(h,r,t),\mathcal{G}^{\prime N}_{(h,r,t)}\}). \tag{4}\]  
Minimizing the _N_-pair translation risk is also carried out by SGD in minibatch mode over the possible triples in \(\mathcal{G}\). If the minibatch size of SGD is \(B\), then \(\mathcal{L}_{npair}\), the _N_-pair translation loss, uses \(B\times(N+1)\) triples at one update. Since the generator _NG_ has to generate \(N\) negative triples, it takes more time than the margin-based ranking loss. However, this cost can be alleviated by using offset-based negative sampling algorithm [31] with a parallel implementation [32]. Algorithm 1 summarizes our proposed method. ```
Input :Knowledge graph \(\mathcal{G}=\{(h_{i},r_{i},t_{i})\}_{i=1}^{|\mathcal{G}|}\), entity and relation sets \(\mathcal{E}\) and \(\mathcal{R}\) Output :Trained knowledge graph embeddings \(\theta\) repeat \(g_{\theta}\gets 0\). // gradient of parameter \(\theta\)  Sample a mini-batch of data \(\mathcal{G}_{batch}\) from \(\mathcal{G}\). for\((h,r,t)\in\mathcal{G}_{batch}\)do \(\mathcal{G}^{\prime}_{(h,r,t)}\gets NG((h,r,t))\). // generate \(N\) negative samples \(g_{\theta}\gets g_{\theta}+\
abla_{\theta}(\mathcal{L}_{npair}(\{(h,r,t), \mathcal{G}^{\prime}_{(h,r,t)}\}))\). // accumulate gradients  end for \(\theta\leftarrow\theta+\eta g_{\theta}\). // update parameters untilconvergence;
```  
**Algorithm 1**Learning Translation-based Knowledge Graph Embeddings by Considering Multiple Negative Triples