esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 3 Energy Efficiency Analyses of Radio-and-Fiber Networks  
Although flexible and robust FiWi networks have emerged as an attractive solution for the realization of today's modern access networks, the low utilization of the optical network elements and high overhead in packet data communication raise an issue of optimizing the EE of FiWi networks. The utilization issue is a consequence of the data traffic variations wherein, during low or no utilization periods, active elements of FiWi networks consume energy as in the periods of moderate or high utilization. Therefore, improving the EE of FiWi networks is a non-negligible challenge and an active research area [6].  
Over the years, many power-saving (PS) software techniques have been adopted to increase the EE of hybrid FiWi networks. These mechanisms differ depending on whether the PS technique is implemented in an optical or a wireless domain. Most research studies are focused on increasing the EE of these two domains separately (for the optical back-end and wireless front-end of the network). However, it has been shown that in terms of EE of hybrid FiWi networks, cooperation between PS mechanisms of both domains achieves better results [20]. In this section, the recent optical back-end and wireless front-end PS techniques are first reviewed, and then joint cooperative PS techniques between these two domains are analyzed. Table 1 summarizes related work on the improvement of EE of R&F networks.
## 3 Design Solution for FBG Inclinometer for Cantilever-Beam-Type Hydraulic Support
### Design Solution for FBG Inclinometer for Cantilever Hydraulic Support  
The cantilever beam is the core component; the cantilever beam selected is made of Q235 steel, and the wire-cutting process was used; the following figure shows a \(72\times 70\times 20\) mm cantilever beam one-piece structure, which is divided into the top beam and the pendulum below. In the top beam below, through the machining a channel of a 1 mm deep, 70 mm long channel for the pasting of the FBG using the \"three points and one line\" way, \"three points\" for the three bonding fixed points on the fiber, the red grating part is placed in the channel, which cannot be bonded, and thus the red line in the channel below is not bonded. Through the \"three points and one line\" approach, 'three points' for the fiber on the three bonding fixed points, the red grating part of the channel can be placed in the channel, but not bonded to the figure below the channel in the red line for the symmetrical placement of the grating string; the structure is shown in Figure 2.  
The cantilever-beam-type hydraulic support FBG inclinometer is composed of four main components. The first component is a wire-cut metal cantilever beam, fabricated through a precise wire-cutting process to ensure dimensional accuracy and structural integrity. The second component is the grating, which serves as the key element for monitoring deformation. It is designed to detect even minute changes in the cantilever beam's state. The third component consists of a knob for securely fixing the cantilever beam and functions as a calibration knob, allowing for accurate adjustment and calibration of the inclinometer's readings. The fourth component is an alloy case, which not only provides mechanical protection to the internal components but also shields against external environmental factors that could potentially affect the inclinometer's performance.  
As depicted in the figure below, the assembly process commences with the use of epoxy resin glue. First, the fiber-optic grating is pre-tensioned using this glue to optimize its sensitivity and response characteristics. Subsequently, it is bonded at three specific points within the fiber-optic groove. This three-point bonding method ensures that the grating is firmly held in place. Notably, a section of the grating is left exposed without any bonding agent. This unbonded section is carefully designed to have no adverse impact on the transmission of fiber-optic stress-strain signals. The model diagram of the cantilever-beam-type hydraulic support FBG tiltmeter is presented in Figure 3, clearly illustrating the spatial arrangement and relationship of these components.
## 3 Results and Discussion
The comparison of the proposed methodology based on the magnetic microwires with the conventional resonant, acoustic, eddy current, ultrasonic and radiograph principles summarized by The Modal Shop, Inc., dealing with the structural vibration and acoustic sensing systems and services for various applications in design and test laboratories as well as manufacturing plants, is presented in Figure 3:3  
From the comparison, it is obvious that the most important advantage of the methodology based on the tensile stress sensing using magnetic microwires is in the possibility of in-flight monitoring and diagnostics, which is not possible using conventional NDT methods. Thanks to these measurements, it is possible to measure tensile stress distribution during the flight to prevent critical loading of particular components or parts of the aircraft construction, the results of which can lead to the loss of stability and potentially to damage in the construction. The methodology can be also used for the defectoscopy of the material structure. During the periodic material monitoring and diagnostics, it is possible to compare the obtained results and so to create a more precise prediction model that can be used for the precise maintenance planning. A significant change in the microwire response can be caused by the overloading of the structure or by the change in the distribution of the tensile stresses in the aircraft construction caused by the structural changes in other aircraft parts or components. In this case, immediate maintenance is required. Furthermore, data from the in-flight monitoring can be used also for the optimization of the aircraft models, leading to more precise results of the simulation. As the research group is interdisciplinary and consists of electrotechnical and mechanical engineers, our further research will be split into several main tasks involving solution of the electronic vibration effects, temperature dependence analysis and calibration of the sensors. The prepared CAD model of the aircraft can be used for further research, whereas it will serve as a basis for further static and dynamic CAE analyses.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
###### Abstract  
The integration of the Smart Pavement Management Center aims to improve the efficiency and quality of road maintenance in New Taipei City. This paper explores the application of the I-ROAD Reporting System and analyzes its effectiveness in providing real-time road condition updates and alerts, which assist road authorities in making timely decisions. Additionally, the study establishes a comprehensive road maintenance lifecycle model, encompassing road construction, maintenance, repair, and milling. This model systematically manages road resources to reduce maintenance costs and extend the service life of roads. Through the optimization of inspection methods and the evaluation and selection of construction techniques, the most cost-effective solutions were identified to improve the efficiency and quality of maintenance work. Lastly, the paper discusses how to optimize the budget for road maintenance within financial constraints by applying scientific budget allocation and management strategies. This ensures the efficient use of funds and that road conditions are maintained at an optimal level. This study explores the use of a road management system for the analysis and optimization of future road maintenance projects. Within a limited budget, it aims to achieve optimal allocation and management of maintenance funds through scientific financial planning, ensuring efficient use of resources to maintain road conditions at an optimal level.  
I-ROAD system; road lifecycle model; optimized road inspection; maintenance budget optimization analysis +
Footnote †: journal: Applied Sciences
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Irsc  
This dataset was created by Indian River State College and consists of network flows and full packet capture [43]. The dataset represents a real-world environment in which the attack traffic has two different types: the controlled version, which is synthetically created by the team, and the uncontrolled version, which are the real attacks. The flow based traffic created with the Silk [44] and the full packet capture created with the Snort IDS [45]. The additional source of flow data was produced from the Cisco firewall using NetFlow version 9. While the authors stated that the dataset is a complete capture with payload and flow data, unfortunately, it is not publicly available.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### Training  
#### 3.7.1 Batched Query Denoising Training  
We train FusionTrack with video clips containing several frames. Since the number of ground truth objects in each video clip varies during training, it is difficult to align the number of track queries between different segments. This prevents the hardware from performing batched parallel training. To unify the number of track queries in different clips and provide negative samples for the tracking branch to assist convergence, we introduce the batched query denoising method. This design aligns the number of track queries in each sample by adding additional fake tracklets.  
Let the video clip length be \(L\) and the batch size be \(b\). For a batched frame containing \(N_{1},N_{2},N_{3},\dots,N_{b}\) real objects for each sample, we set the target track query length for that frame to be \(N_{t}=\max(N_{i})\times k_{denoise}\). We then unify the number of track queries in each sample to the target length by adding or removing fake tracklets according to the total number of tracks in each sample.  
Preferentially, low-score detections are randomly selected, and their appearance features and predictions are used to initialize fake tracks. Under some rare situations when there are not enough low-score detections to initialize all fake tracks, random appearance features and bounding boxes are used instead. Figure 6 shows an example of an intermediate frame from a batched training sample containing two different clips.
## 4 System Model, Algorithms, and Security Model
### Security Model  
We define the security model for SP-MAACS through a security game between adversary A and challenger C. We assumed that A can corrupt at most K-1 AAs. Let \(\mathrm{K_{c}}\) denote the index set of corrupted AAs and \(\mathrm{K_{uc}}\) denote the index set of uncorrupted AAs, where \(\mathrm{K_{uc}}=\mathrm{K\backslash K_{c}}\). The steps of the game are as follows:  
**Setup:** C executes the algorithms GlobalSetup, CASetup, and AASetup and transfers the GPP, MPK, and \(\cup_{k=1}^{K}APK_{k}\) to the adversary A. Assume the adversary corrupts \(\mathrm{K_{c}}\) AAs such that \(\mathrm{K\backslash K_{c}}\
eq\Phi\). The challenger C passes the secret key \(\left\{\mathrm{ASK_{k}}\mid k\in\mathrm{K_{c}}\text{ to A}\right.\).  
**Phase 1:** Adversary A can obtain the secret keys for the AAs who have been corrupted.  
**CAKey queries:** For these queries, challenger C responds by CAPK\(\mathrm{gid}\) and \(\mathrm{CASK_{gid}}\).  
**AAKey queries:** For the attribute set, the adversary submits \(\mathrm{S_{gid,k}}\) and CAPK\(\mathrm{gid}\) to C, where \(k\in\mathrm{K_{uc}}\). C returns the \(\left\{\mathrm{ASK_{gid,k}}\right\}_{k\in\mathrm{K_{uc}}}\).  
**Challenge:** If adversary A finds that phase 1 is complete, it sends to C two messages \(\mathrm{M_{0}}\), \(\mathrm{M_{1}}\), which are equal in length, and two challenge access structures \(\psi_{1}^{*}=(W_{1},\ \rho_{1},\ Z_{1})\) and \(\psi_{2}^{*}=(W_{2},\ \rho_{2},\ Z_{2})\). Here the condition is that \(\psi_{1}^{*}\) and \(\psi_{2}^{*}\) cannot be satisfied by any attribute key query performed in phase 1. A random coin \(\mathrm{c}\in\left\{0,1\right\}\) is flipped by C; then, it sets \(CT_{\psi_{c}}^{*}\gets Encrypt(K_{c},\ \psi_{c},\ GPP,\cup APK_{k})\) and passes this challenged ciphertext to A.  
**Phase 2:** As in phase 1, adversary A can again obtain adaptive secret key queries.  
**Guess:** Adversary A gives its guess \(\mathrm{c^{\prime}}\) of \(\mathrm{c}\) as output and wins the game if \(\mathrm{c^{\prime}}=\mathrm{c}\). Probability \(Pr\) of being \(\mathrm{c^{\prime}}=\mathrm{c}\), i.e., \(\left|Pr[\mathrm{c^{\prime}}=\mathrm{c}]=\frac{1}{2}\right|\) is called the advantage of adversary A. To compute \(Pr\), A and C choose random bits.  
**Definition 6**: _Our SP-MAACS scheme with a privacy-preserving feature is fully secure since no probabilistic polynomial-time (PPT) adversary has a non-negligible advantage in the above game._
# New Insights into the Mechanism of Spatiotemporal Scent Accumulation in Orchid Flowers
## 2 Results  
### The VOCs Emitted by KOVA Flowers  
Two benzenoids, including benzaldehyde and benzyl alcohol, a sesquiterpene (_beta_-Farnesene), and several monoterpenes, including linalool, _beta_-Ocimene, _cis_-allo-Ocimene, _cis_-Citral, citral, and geraniol, were detected in the KOVA floral scent during D7 and D8 (Figure 2). The results showed that two benzenoids, _beta_-Farnesene, and linalool were detected from KOVA flowers at both stages D7 and D8, and the relative contents of benzenoids and _beta_-Farnesene emitted by KOVA flowers at D7 were obviously higher than those at D8, while the relative content of linalool showed the opposite trend. Additionally, five monoterpenes, including geraniol, _beta_-Ocimene, _cis_-allo-Ocimene, _cis_-Citral, and citral, were only emitted by flowers at D8 (Figure 2). These results suggest that monoterpenes are the main VOCs constituting the floral scent emitted by KOVA flowers at the full flowering stage (i.e., D8).  
Figure 2: The relative contents of VOCs emitted by KOVA perianths during flowering. The relative content represents the result calculated by the external standard method based on data produced by GC. The data are the mean \(\pm\) SD from three biological replicates.
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Optimization of the Handshaking and Data Transmission Schedules  
To make the transmission schedule more compact, the time slots of the TDMA handshaking and the transmission schedule of data packets are optimized according to the propagation delay between nodes in the proposed method. The optimizations of the TDMA time slot and the data packet schedule are similar except that there are only one channel and a fixed number of source nodes in the former while there might be more than one channel and a variable number of source nodes in the latter. Note  
Figure 4: Merging the request to send (RTS), clear to send (CTS), and acknowledgement (ACK) stages into one control channel.  
Figure 3: Implementing the typical transmission process with a four-stage pipeline.  
that the transmission sequence and time of the nodes will both affect the transmission efficiency [18]. Several methods can be used for the transmission sequence and time optimization, such as the dynamic programming and greedy algorithm. However, when the channel and source node number are large, large computational complexity is required in searching the best channel and transmission time using dynamic programming. Therefore, the greedy algorithm is employed in the proposed protocol to optimize the schedules for both the control and data packet transmission. Furthermore, two suboptimal assumptions are also adopted to simplify the discussion. The first is that a node can only transmit or receive a data packet in the same transmission cycle. The second is when node \(i\) sends out its packet before node \(j\), the packet from node \(i\) will arrive at both destinations earlier than that from node \(j\). These assumptions lead to the condition of collision-free transmission as:  
\[T_{j}\geq T_{i}+L_{i}+\max\Bigl{(}D_{i,i^{\prime}}-D_{j,i^{\prime}},D_{i,i^{ \prime}}-D_{j,i^{\prime}}\Bigr{)}, \tag{1}\]  
where \(T_{i}\) and \(T_{j}\) denote the sending time of the packets from node \(i\) and node \(j\), respectively. \(L_{i}\) is the duration of sending out the packets from node \(i\). \(D_{i,i^{\prime}}\) denotes the propagation delay from node \(i\) to the destination of node \(j\).
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 4 Experimental Scenarios  
To evaluate their proposed stranded-NN model, the authors experimented with two different datasets:  
Stranded-NN implementation using compressor temperature data. A temperature dataset, provided by two sets of the 1402 compressor of the Hellenic petroleum company. Each set includes a series of four temperature sensors (a total of eight temperature measurements/minute).  
Stranded-NN implementation using pumps axial acceleration data. The measurements of normal and imbalanced pump cases are provided by the Mafaulda dataset [42]. The sensors used include three industrial accelerometers in the radial, axial, and tangential directions and a triaxial accelerometer (a total of six measurements per second).  
All sensors were coupled to the pump's axis. The pump was 250 W (0.33 Hp) with a 16 mm axis diameter and 520 mm of axis length, and a coupling bearings distance of 390 mm. A total of six measurements were provided by the accelerometers every 5 s. In addition, the measurement data of fixed rotational pump speeds of 737 rpm were used to simplify the problem further. Furthermore, it is hard to distinguish pump vibrations using vibration sensors at the pump's mounting plate at such low rotation speeds. The acceleration data unit of measure was m/s\({}^{2}\).  
For Scenario I, of compressor temperatures, a stranded-NN model has been constructed with strands of data inputs \(n=16,48,80\) and no dropout layers for real-time classification (see Figure 11 at Appendix A.1), signifying a batch of measurements of 1 min for \(n=16\), 3 min for \(n=48\) and 5 min for \(n=80\). For close-to-real-time measurement, strands of data inputs \(n=160,480,960\) were used for 10, 30, and 60 min accordingly (see Figure 11 at Appendix A.2).  
For Scenario II of pump acceleration measurements, a stranded-NN model was constructed with strands of data inputs \(n=12,30,60\) and no dropout layers for real-time classification (see Figure 11 at Appendix B.1) and strands for data inputs \(n=150,300,600\) for close-to-real-time classification (see Figure 11 at Appendix B.2).  
For both scenarios, the annotated data use five classes, indicating maintenance emergencies or critical machinery operations. The first class indicates normal behavior. The second class indicates close-to-normal machinery behavior that needs future maintenance attendance. The third class indicates stressed behavior that requires persistent monitoring and/or immediate maintenance. The fourth class indicates critical alert behavior that requires response actions, while the fifth class represents catastrophic cases that will stress the equipment beyond its intended use, as defined by machine specifications.
## 3 RL-Based Resource Allocation Algorithm
### Link Data Compression  
DNN compresses the local observation data of each V2V link. Each V2V link first observes the surrounding environment to acquire its transmitting power \(P_{j}^{d}\) and the combined interference power from other links \(I_{j}=(I_{j}[1],\ldots,I_{j}[i],\ldots,I_{j}[I])\). Considering the impact of V2V links on V2I links, the observation data of the \(j\)-th V2V link must also include the interference channel gain from the V2V link to all V2I linkswhich are denoted as \(H_{j,B}=(H_{j,B}[1],\ldots,H_{j,B}[i],\ldots,H_{j,B}[I])\); it can be estimated at BS and broadcast to all V2V links within the coverage area of BS, resulting in low signaling overhead [10]. In [22], it is assumed that the power gain of the current channel can be obtained by delayless feedback at the vehicle transmitter on the \(j\)-th V2V link, which is represented as \(H_{j}=(H_{j}[1],\ldots,H_{j}[i],\ldots,H_{j}[I])\).  
In this case, the observed data of the \(j\)-th V2V link can be written as:  
\[O_{j}=\Big{\{}P_{j}^{d},I_{j},H_{j,B},H_{j}\Big{\}}. \tag{7}\]  
The observed data \(O_{j}\) is compressed using DNN on each V2V link; the compressed data \(f_{j}\) output by DNN is fed back to DQN at BS. Here,\(f_{j}=\Big{\{}f_{j,k}\Big{\}}\) is also known as the feedback vector of the \(j\)-th V2V link, where \(f_{j,k},\forall k\in\{1,2,\ldots,N_{j}\}\) refers to the \(k\)-th feedback element of the \(j\)-th V2V link, \(N_{j}\) represents the feedback number learned by the \(j\)-th V2V link. \(f_{j}\) will also serve as input to DQN for the reinforcement learning process.
# A Transfer Learning Method for Pneumonia Classification and Visualization
###### Abstract  
Preunonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.  
transfer learning; pneumonia; classification; X-ray; convolutional; deep learning Article
## 3 Classification
### Communication Technologies
#### 3.6.6 700 MHz ITS Band  
[PERSON] et al. [28] have developed a system for V2P communication in 700 MHz ITS band (in accordance to the Japanese standard for ITS). It evaluates various channel access mechanisms for V2P system. It serves as a proof-of-concept for co-existence of V2P, V2V and V2I systems in the 700 MHz system.
## 5 Simulation Verification
### Simulation of Flux Identification  
For the online identification of the flux linkage, three different adaptive laws are chosen for comparative simulation. To verify the dynamic identification performance, the real flux linkage is set to increase by 20% at 0.5 s and restore the original value at 0.6 s. The results are shown in Figures 9-11. From Figures 9 and 10, the adaptive laws based on PI and switched PI are greatly affected by noise and show an unsatisfactory performance. From Figure 11, the fluctuation amplitude of the flux linkage is much smaller, which displays that the identification accuracy of ADRC adaptive law is superior to the other two methods. In addition, the tracking speed of the ADRC adaptive law is as quick as the PI and the switched PI.