esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments  
We designed three experiments for the proposed MD4-AQF method. First, we investigated the agent's learning process for MD4-AQF. Second, we contrasted the training progress of MD4-AQF and another MFDRL method (D3 ynQN), which was the same as our method but with its \"multi-dimensional network\" and \"aligned value fusion\" ablated. Third, we compared the performance of the agent trained by MD4-AQF and a conventional RTSC method. Table 1 shows the setup of the experiments.  
The experiments were conducted via a virtual intersection modeled by the traffic simulation software PTV Vissim 11.00-14. Signal control was implemented through the Vissim COM interface for Python 3.9.13. Neural networks were built using PyTorch 2.0.0.  
The detailed geometry of the intersection and its peak-period traffic demand scenarios can be found in [25]. Briefly, a street had three lanes in each direction and was widened to four lanes when approaching the intersection (see Figure 7). The typical DRBPS in Figure 3 was employed. The minimum greens were 15 s for through phases and 5 s for left-turn phases. The maximum greens were 25 s greater than the minimum greens. The yellow change and red clearance intervals were 3 s and 2 s, respectively. Each simulation run lasted for \(\mu=4200\) s, with the first 600 s being the warm-up period.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Experiment** & **Involved Method** & **Analytical Tool** & **Purpose and Expected Outcome** \\ \hline \#1: agent learning & MD4-AQF & Line graph (Figure 8) & To verify whether the proposed method enables the agent to learn and improve its policy. The intersection performance is expected to gradually improve throughout the learning process. \\ \hline \#2: versus MFDRL method & MD4-AQF & Line graph (Figure 8) & To verify whether the proposed method trains the agent more effectively than another MFDRL method, D3 ynQN. The proposed method is expected to achieve better intersection performance than D3 ynQN, both during training and at the end of training. \\ \hline \#3: versus conventional method & MD4-AQF & Box plot (Figure 9) & To verify whether the policy learned via the proposed method outperforms the conventional method FASC. The learned policy is expected to yield better intersection performance compared to FASC in most of the tested traffic demand scenarios. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of experiment setup.  
Figure 7: Layout of the virtual intersection for experiments.  
Figure 8: Learning curves for MD4-AQF and D3 ynQN. The solid line represents the median of the average vehicle delay across 100 simulation runs for each evaluation, and the shaded area represents the range between the top and bottom quartiles.  
Figure 9: Box plots of the average vehicle delay caused by D3 ynQN, MD4-AQF, and FASC.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture
### Interacting Multiple Model Filter Structure  
The IMM filter, which compared with other multiple model estimation algorithms, has attracted wide attention in virtue of their higher performance and lower computational cost [26; 27]. Therefore, the IMM estimator has used to obtain the state estimates under various filters on the basis of the model probabilities in this research. The IMM filter method is composed of three parts: interaction and mixing, mode probability update, and combination.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### EEDC-AA  
EEDC-AA [33] is an energy efficient data collection protocol that assumes a network consisting of three kinds of nodes: (1) Sensor nodes that sense the phenomenon under consideration, (2) collector nodes that gather data from sensor nodes (through single or multihop communication), and AUVs,  
Figure 7: Greedy route establishment phase.  
which gather data from collectors, and offload it upon surfacing. Moreover, the protocol uses two types of underwater communication media. Acoustic communication is used between sensor nodes and collectors, while optical communication is used between collectors and AUVs. The proposed scheme aims to minimize the energy efficiency and avoid connectivity holes, while performing data gathering based on a value of information metric. Value of information is strongly connected to time within which the data can be considered useful. Therefore, data with higher value of information is given preference in the data gathering process. Relays are selected depending upon the available energy. A node is chosen as relay if its available energy level is above a certain threshold level. EEDC-AA proposes a two-phase data gathering mechanism. The first phase deals with routing the collected data from sensor nodes to sink nodes. Meanwhile, the second phase is related to value of information-based path planning for a AUV to retrieve data from underwater sink nodes. EEDC-AA can prioritize data based on the value of information, and can respond to critical events on a priority basis. However, it does not explain how value of information, which is an important metric, is estimated. The proposed scheme improves network lifetime by reducing the chances of void creation through load balancing.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### Authentication of Variable Parts  
The SAODV protocol uses a hash chain to protect the variable part in the route request (RREQ) and route reply (RREP) messages. Hash chain is realized by repeatedly applying one-way hash function to a random number. Each node that receives a RREQ or RREP message can verify the hops field to ensure that it is not maliciously reduced by the attacker. The process of RREQ or RREP routing messages is shown in Algorithms 1.  
```
1:for each RREQ or RREP do
2: Generate a random number (seed)
3: Set the Max_Hop_Count field, and fill the value of the Time field in the IP header
4: Set the Hash field, and fill the value of seed
5: Set the Hash_Function field, which indicates the type of hash function used
6: Set the Top_Hash field: Top_Hash = \(h^{\textit{Max\_Hop\_Count\_Hop\_Count\_Hash}}\)
7: Verify the hop count information: Top_Hash = \(h^{\textit{Max\_Hop\_Count\_Hop\_Count\_Hash}}\)
8:if the result is equal to the value in the Top_Hash field then
9: the hop number field is correct
10:else
11: the hop number field is wrong
12:endif
13: Calculate the Hash value to record the new hop: Hash = \(h\)(Hash)
14:endfor
```  
**Algorithm 1** The Process of RREQ or RREP Routing Messages  
The _Hash_Function, Max_Hop_Count, Top_Hash,_ and Hash fields are transmitted in the AODV extended message. The format definition of the extended message is shown in Figure 2. The RREQ message format of SAODV protocol is increased by 20 bytes on the basis of AODVformat. The increased part is _Type2_, _Length_, _Hash_Function_, _Max_Hop_Count_, _Top_Hash_, _Signature_, _RREP Signature_ (_optional field_), _Hash_.
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 2 Pull vs. Push Communication Models  
In a _pull-based communication model_, the user issues a request for any specific information object. In contrast, in a _push-based model_ interested parties get the information if they have previously subscribed it, and information gets distributed when it is available. Therefore, producers push information periodically or based on events to consumers.  
Push-based models, which have been around for quite a while, became popular in the late 90's with the advent of PointCast [8]. This software platform allowed users to define an interest profile and delivered data to the user based on such interest. Data delivery followed the regular IP-based communication, i.e., content was provided via a point-to-point transfer. The relevancy of push-based technology at the time was the possibility to deliver personalized data. To the end-user, data seemed to be \"immediately\" available, as the user did not have to explicitly request such data by clicking on a button, for instance. However, due to the fact that the underlying network layers would still operate on a unicast basis, push-based technology had a fast decline.  
With the advent of wireless and cellular networks and the need to support content-based distribution in real-time or close-to-real-time to a large and varying number of users, push-based communication has been re-ignited.  
In current IoT communication solutions such as RESTful HTTP or _CoAP_ (_Constrained Application Protocol_) [9], communication follows a _request-response_ strategy, where IoT devices acting as _data producers_ transfer information to a server (e.g., gateway) or to end-user devices acting as _data consumer_ after consumers issue a pull request.  
In publish/subscribe broker-based models (rf. to Figure 1) such as the ones supported by AMQP, MQTT, or even _OLE for Process Control (OPC) Foundation Open Platform Communications-Unified Architecture (OPC-LIA)_[10; 11], producers send information to brokers (mediating entities) which then relay the information to consumers that a priori subscribed to the respective types of information. In such cases, data transmission is initiated by producers and consumers cannot \"regulate\" the data transfer in any way. Hence, the push-based communication can be performed between producers and broker, as well as from broker to consumers. This push-based model is, however, still based on unicast and hence, current IP-based publish/subscribe solutions mimic a 1-to-N transmission, unless an IP multicast infrastructure is set between broker and consumers. Furthermore, publish/subscribe broker models fall short as brokers cannot control data transmission by producers and consequently, they may easily be overloaded in large-scale scenarios.  
Even though ICN publish/subscribe approaches (rf. to Figure 2 consider by default pull-based communication, push-based support has been envisioned in the original design of ICN, as shall be further discussed in Section 3.
## 4 Seismic Fragility Assessment Results  
The result of seismic fragility assessment based on IDA is summarized for each component wall of three analytical models in Figure 15, and Tables 10 and 11. All the shear wall groups were designed to satisfy shear force demand with an amplification factor 1.2 to the mean peak shear force in accordance with the AIK guidelines. The seismic fragility curves in Figure 15 were constructed based on the failure mode that occurred at the lowest intensity measure among four failure modes investigated in the IDA. That is, if one of the four EDPs does not satisfy its acceptance criteria earlier than others, the corresponding intensity measure was used for the calculation of fragility curves. FEMA P695 requires the collapse probability of individual building model to be limited below 20%, and the average collapse probability for the performance group comprised of different structural models with the same structural system to be limited below 10%. All the three analytical models have a collapse probability less than 20% in Table 10 if the maximum collapse probability among the component walls is defined as the collapse probability of the analytical model. Additionally, the average collapse probability among the three analytical models is 8.1%, which is below the 10% limit. As a result, all of the three analytical models satisfy the requirement for collapse prevention as prescribed in FEMA P695. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Analytical** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Component** \\ **Wall** \\ \end{tabular} & \begin{tabular}{c} **Length (m)** \\ \end{tabular} & \begin{tabular}{c} **Axial Force** \\ **Ratio (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **MCE Collapse** \\ **Probability (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Pass/Fail** \\ \end{tabular} \\ \hline \multirow{4}{*}{Model A} & R1 & 2.5 & 30 & 9.8 & - \\  & **R2** & **2.5** & **30** & **9.9** & **Pass** \\  & T1 & 3 & 15 & 3.7 & - \\  & T2 & 3 & 15 & 5.5 & - \\  & R3 & 9 & 15 & 7.3 & - \\ \hline \multirow{4}{*}{Model B} & R1 & 2.5 & 30 & 4.4 & - \\  & **R2** & **2.5** & **30** & **5.6** & **Pass** \\  & T1 & 3.5 & 15 & 0.9 & - \\  & T2 & 3.5 & 15 & 1.0 & - \\  & R3 & 9 & 15 & 5.5 & - \\ \hline \multirow{4}{*}{Model C} & **R1** & **2.5** & **30** & **8.8** & **Pass** \\  & R2 & 2.5 & 30 & 7.4 & - \\ \cline{1-1}  & T1 & 4 & 10 & 2.4 & - \\ \cline{1-1}  & T2 & 4 & 10 & 3.5 & - \\ \cline{1-1}  & R3 & 9 & 10 & 5.3 & - \\ \hline \multicolumn{4}{c}{Performance group (Average)} & **8.1** & **Pass** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of collapse probability for analytical models designed for the shear force amplification factor = 1.2.
## 4 Validation and Calibration
### Atmospheric Enclosure  
A detailed schematic diagram of the home-made atmospheric enclosure is presented in the following figure (Figure 4). Atmospheric chambers provide a controlled environment to study the response, stability, precision and accuracy of the laser-based instrument at a stable gas temperature, pressure and concentration. A home-made enclosure was made from stainless steel for spectroscopic applications (vacuum, extreme temperatures, non-corrosive,... ). The tightness is insured by a joint fixed on the lid. This atmospheric enclosure is double-walled to be thermalized with a cryogenic fluid (glycol allowing temperature control from \(-\)25 \({}^{\circ}\)C to \(+\)50 \({}^{\circ}\)C) injected continuously by a FP50-MA Chiller (Julabo, Seelbach, Germany). Temperature stabilization is carried out by a home-made PID software system. Temperature measurements are based on four PT-100 class 1/10 temperature sensors connected to a 24-bit thermometer. A thermal insulation layer covers the whole enclosure to avoid heat-flux exchange with the outside ambient atmosphere. Five electronic valves were connected to this enclosure dedicated for a pumping system (valve number 1 on the Figure 4 below) and different calibrated gas cylinders provided by the by the \"Laboratoire des Sciences du Climat et de l'Environnement LSCE\" (connected separately to the valves 2 to 5 (see Figure 4)). A MKS 626A-13 TDE pressure transducer having a range of 0-1000 Torr is connected to continuously control the interior gas pressure. The range of this pressure transducer is sufficient for the calibration process of the instrument. All of those elements listed above (electronic valves, pressure transducer, temperature sensors) were connected to the computer via a USB-TEMP-AI by Measurement Computing (Norton, MA, USA) in order to be controlled by our home-made-developed automated command and control software. This software is also used to turn on and control the AMULSE. This system allows variation of the pressure between 0 and 1200 mbar (with fine tuning function) for a temperature range between \(-\)25 \({}^{\circ}\)C and \(+\)50 \({}^{\circ}\)C. The standard deviation of the pressure and the temperature measurement (0.01 mbar and 0.01 \({}^{\circ}\)C respectively) were held at 1 Hz during 1.5 h.
## 2 Materials and Methods
### Data Processing: Identification Procedure
#### 2.3.4 Identification Procedure  
The fish identification task was performed for classification in the closed group (with a known number of individuals), where the number of classes corresponded to the number of individual fish in each experiment (300 sea bass and 32 carp in the short-term scenario and 32 sea bass and 32 carp fish in the long-term scenario). Classification was performed separately for both the short-term and long-term data sets. The nearest-neighbor classifier was used to classify the fish, while the similarity between ROIs was used as the measure.  
Short-term experiment: Each fish was represented by 6 to 11 ROIs of the images from one data set. One randomly selected ROI was used as the unknown fish, and the rest were used as representative ROIs (database) of the fish. The unknown ROI of each fish was compared with all representative images of all fish in the database. The unknown fish was classified as the most similar fish in the database. The same process was applied to each data set separately. In total, 300 seabass and 32 carp (classes) were used.  
Long-term experiment: Each fish was represented by 6 to 11 ROIs of the images from one data set. Images of one fish from the first data set were compared with images of all fish from the second data set. This process was repeated for all fish from the first data set. For carp, this procedure was repeated for all combinations of the four data sets. Three images of the fish from one data set were used as the reference set, and three images of the fish from the other data set were used as the test images. The process used the same similarity measure used for the short-term data set. A test of pattern uniqueness was conducted for the first and second data sets separately for the seabass, as well as for the first, second, third, and fourth data sets, using the same approach described for the short-term classification of 32 fish (32 seabass and 32 carp classes, separately). The results of the classification were confusion matrixes describing the similarity between all combinations of individual fish.  
Manual (human-based) identification for the long-term experiment involved exploring the skin patterns via human observation. Five randomly selected fish were chosen for identification. One image of each fish from the first data set and one image of each fish from the last data set were introduced to two people. Both individuals performed the identification independently.  
Figure 7: HOG visualization for ROIs. The left image is the ROI1 for carp resized to 64 by 64 pixels, where the white holes are the scales of the fish, and black lines are the connecting parts of the scales. The right image represents the ROI(LL) for seabass resized to 64 by 64 pixels, where the black line is the lateral line. The rectangle in the left-top corner of both images visualizes the HOG gradients used for pattern parametrization. The gradience are tiny white lines, which correspond to the orientation of the edges in the image. For common carp it follows the shape of the scale and for the seabass it follows the lateral line.
## Materials and Methods
### Setup for Tracking of Acoustic Waves in the Pipe Wall  
For laboratory-scale tests a stainless-steel pipe of 6.25 m length, inner diameter of 40.7 mm and outer diameter of 48.3 mm was simultaneously equipped with two separate sensor configurations.  
Figure 1: Program flow chart illustrating the procedures used for data evaluation together with influencing variables.  
The pipe was open on both ends. Each configuration could be connected to the C-OTDR system independently. They are schematically depicted in Figure 2.  
Configuration 1 represents a fully distributed system, where the fiber is helically wrapped around the pipe on the full length. Pitch of the fiber was 2 cm, so that 7.9 m of fiber correspond to 1 m on the pipe. Altogether 39.5 m of fiber were applied to the pipe in this manner.  
Configuration 2 corresponds to a quasi-distributed setup with two localized sensors regions at the beginning and at the end of the pipe. Each of the two localized sensors sections consists of approximately 30 m of fiber densely wrapped around the pipe covering a region of 3.7 cm to 3.8 cm. They are separated by two loose coils of fiber (15 m each), which are decoupled from the pipe, to ensure separation of signals originating from the two sensor regions. The distance between the centers of the two sensor regions amounts to 5.98 m.  
In each setup, a coil of 200 m of fiber was introduced between the measuring system and the applied fiber stretch under test.  
Short mechanical impacts were produced by manually hitting the pipe with a metallic device either on the front face (A) or on the side (B) as indicated in Figure 2.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 3 Experimental Method
### Experimental Solution  
To analyze the effects of LEO enhancement on GPS time transfer performance, a sole GPS time transfer experiment was carried out. We used the following two experimental schemes for the comparative analysis of time transfer performance:  
Solution A: Time transfer using a sole GPS system.  
Solution B: Time transfer using GPS combined with LEO satellites.  
To verify the applicability of LEO-augmented GPS time transfer at different latitudes and its performance with different numbers of satellites, we designed time transfer links and simulated different numbers of satellites.
# Parametric Study on the Effect of Rail Dampers on Track Decay Rate
## 1 Introduction
studied the influence of tuned rail dampers on the decay rate of the rail, considering the particular case of a metro track with an egg fastening system [29]. [PERSON] et al. have undertaken laboratory vibration studies to develop a tuned rail damper aimed at mitigating the vibration of the metro tracks [30]. This paper is dedicated to the theoretical assessment of the ability of a rail damper with a mixed damping system (rubber-oil) to reduce rail vibration on light rail tracks such as those on some sectors of the railway network. Its structure is different from the known constructions. It has a cylindrical body inserted with a small radial clearance inside a tubular piece and fixed by this via rubber rings. A thin oil film fills the space between the two pieces and contributes to the damping. Two caps with sealing devices are mounted at the ends of the tubular piece. The tubular piece is dressed in a rubber mantle that is glued to both rail web and rail foot. Rail damper is designed to match the dimensions of lightweight type 49 rail. Previously, this type of rail damper has been presented, and the parameters of its two degrees of freedom model have been experimentally determined [20]. An analytic model of the track with rail dampers consisting of an infinite Timoshenko beam with discrete attached oscillators is used. Bending waves in rail, rail receptance, and TDR are calculated based on the frequency-domain Green's function associated with the Timoshenko differential operator. Parametric study considers soft/stiff rail pad, tampered/settled ballast, and sleeper bay. Numerical results show the effectiveness of the rail damper with mixed damping system is between 400 and 500 and 1250-1600 Hz for tracks with soft or stiff rail pads.