esa-sceva/satcom-chunk-collection huggingface dataset:
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 6 Experiments  
We evaluate the proposed MAAF by comparing three MARL algorithms: IPPO (Independent PPO) [23], which consists of independent PPO agents [19] without value sharing; MAPPO (Multi-Agent PPO) [23], which uses a shared value network by observation sharing; and TarMAC [11], which shares attention-based messages. We construct messages by adding a branch from the internal hidden representation to obtain the communication messages. The proposed methods have two variants for message aggregation: RNN-based and attention-based. For RNN, we use a two-layer GRU and a single-head for attention-based aggregation. The base policy PPO is implemented by CleanRL [48]. We train MARL agents for 1M time steps for base policy and another 1M for the adaptation. We use four types of environments: multi-particle environments (MPE) [49] the spread and the tag, and SMAC (Starcraft Multi-Agent Challenge) [50] 3M (3 Marines) and 8M. Figure 5 shows examples of environment states. Agents in the spread environment must spread as far as possible to each other while staying near target locations. Predator agents in the tag environment must tag as many prey as possible to achieve a higher return. As agents share rewards, reported MARL rewards are single-agent rewards. We train the baselines for three seeds and PPO clip coefficients (0.2, 0.3, 0.4). We report the best results obtained by the optimal clip coefficient. For MAAF, we train for three seeds and fix the clip coefficient of 0.3. We report the best message type in combinations \(\mathcal{S}_{\text{comb}}=(o,a,a,p,op,ap,ap)\) where \((o,a,p)\) represent observation, action, and probability distribution, respectively. The base policy network has 3 layers of 128 hidden dimensions with Tanh activation. The message generator is a 3-layer MLP with 128 hidden dimensions and Tanh activation. The dimension of generated messages is eight. The adapter policy is the same architecture as the base policy except that the input is an eight-dimensional hidden representation of messages.  
Figure 4: Graphical illustration of message generation. The combination of four types generates message \(M\) for cooperative agents. Optionally, a user can feed a new message input type to utilize communication.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 4 Methods  
### Motivation  
As mentioned earlier, visualized interpretation with XAI methods for image classification that provides the output in the form of a heatmap is often acceptable. If the classification accuracy is high enough, the ensuing XAI result also tends to proceed properly. However, current XAI methods are not perfect, and often fail to separate an object from the background, eventually only highlighting the high-contrast object contour. Figure 1 shows the input image and its attribution score obtained using three XAI methods for image classification based on VGG16 trained upon ImageNet data. Deep Taylor shows the positive score distribution on the high-contrast object contour. Integrated gradients and LRP show relatively random positive-negative score distributions, yet still highlight the general contour of the ball. However, when humans attempt to interpret such XAI output, visual perception is involved and assists in interpretation. Visual perception expects general or partial shapes of certain possible candidates and processes the attribution score of each pixel accordingly rather than as individual numbers. The involvement makes interpretation possible, despite the random negative score distribution near the contours. Such a series of phenomena can be seen as an interference of human visual perception caused by thedifference in cognition levels between a human and machine (or model). Although there are several studies focusing on object segmentation, interference of human perception is inevitable until perfect segmentation is achieved [21].  
Based on the difference between human and machine perception on image classification, we attempted to interpret the attribution score obtained from deepfake voice detection. We focused on the general distribution of the scores for contextual information following human perception and compared it with the traditional method used for the spectrogram-based audio analysis.
## 7 Results and Limitations  
We validated our approach on \"real use case\" at CSTB. We used TG which summarize the main requirements of seven Unified Technical Document (DTU) currently applied in France. We designed our rules database by extracting regulation constraints and transformed them into semi-formal language (SBVR) and formal language (SPARQL) using many steps. These transformations have been handled manually by domain experts. They were able to model 100% of TG constraints into SBVR.  
Although the interpretation of experts helps in translating most of the constraints from text to SBVR, some of them remain non-transformable into SPARQL. These cases correspond to the \"fuzzy\" constraints that contain information defined in a qualitative way (e.g., \"a short distance\"), or that contain \"common knowledge\" [5] (e.g., \"The recovery of the ridge tiles is in the opposite direction of the winds rain dominant\"--Technical guides, \"The tile roofs\" page 75).  
The current limitation of our technical guides is that they collect detailed execution data featuring a wide range of situations. These types of rules are called \"Implementation rules\", easily representable in SBVR, are the largest part of the rules that cannot be formalized in SPARQL. These rules represent a large proportion of regulatory constraints identified in the TGs (in this case 70%).  
Example of implementation rules (Table 3):  
As final results, with the help of CSTB experts, we identified about 177 SPARQL queries and 177 elementary processes. This result is about 30% of the SBVR rule modeled.  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Implementation rules**} & \multicolumn{1}{c}{**SBVR formulation**} \\ \hline \(\epsilon\)In the case of Canal tile, it typically runs in a mortar or flashing bardelis embedded and sealed in the wall.\(\flat\)_Technical_ & If \(\epsilon\)**ile canal then it is obligatory that it _runs_** \\ _guides ”The tile roofs” page76_[30]. & in a mortar or flashing bardelis embedded \\ \hline \end{tabular}
\end{table}
Table 3: Transforming implementation rules into SBVR formulation.
## 2 Data and Method
### Data
#### 2.1.2 LiDAR Data  
Airborne laser point cloud data are carried out by an airborne lidar scanning system, combined with IMU/DGPS assisted aerial measurement technology. Specifically, a Cessna 208b aircraft is selected to carry a riegl-vq-1560i lidar load (seamless integration of inertial navigation IMU and GNSS system). The Cessna 208b aircraft is also observed and obtained jointly with the ground CORS station and artificial base station. The flight height of LiDAR data acquiring is set at an average of 1000 m above ground level (AGL), and the laser records four returns per laser pulse. With a 20% overlap of flight lines, the average laser point density is 20 ppm\({}^{2}\). The reported horizontal accuracy is 15 to 25 cm, and vertical accuracy is about 15 cm for the mission specifications of this project. And all the details are listed in Table 1.  
Figure 1: _Cont._
## 3 Results
### Existing Solutions
#### 3.3.1 Wayfinding Systems in Crowd Management  
The effective communication of evacuation orders and controlling large numbers of crowds are the main challenges in cruise ship evacuations. Several studies have underscored the importance of wayfinding systems in leading passengers to safety and maintaining orderliness. [PERSON] et al. [23] underline that there should be a minimum initial dissemination rate of 50% (initial proportion of passengers who receive emergency information, such as an evacuation order, immediately when it is disseminated) for the wayfinding system to accelerate the evacuation and prevent bottlenecks, thus being successful in timely evacuation. Similarly, improving route guidance systems to reduce perception times can significantly alleviate congestion during evacuation. Consistent findings such as these imply that the design and implementation of wayfinding systems need to take account of human behavior when emergencies occur. During times of pressure, it is common for passengers to depend on their visual surroundings and the actions of others as they move through an unfamiliar environment.  
[PERSON] et al. [40] suggest that, in these areas, advanced information technology could be used to better assist with the allocation of resources as well, aiming to achieve effective communication between operatives through emergency response teams. At the same time, another important issue towards success is that passengers must have clear and consistent messaging from the front-line workers on site, something that, in turn, can quite effectively serve in reducing confusion and panic. The role of IT applications should not be underestimated; both dynamic and adaptive systems appear to be promising in the search for optimal evacuation routes, in addition to static wayfinding. The Improved Artificial Fish Swarm Algorithm (IAFSA) is an optimization method designed to address multi-route planning problems for evacuating all passengers and crew on cruise ships and which, unlike the basic single-route artificial fish swarm algorithm, uses the entire swarm of simulated fish to model the behavior of passengers and crew during evacuation. This method offers the opportunity to dynamically adjust evacuation routes based on real-time crowd behavior and environmental conditions [41]. This algorithm uses queuing delays and distribution strategies for alleviating congestion, which is considered to be a milestone in managing real-world evacuation simulations. The incorporation of these computational models into wayfinding systems can possibly improve peoples' responsiveness and their performance of the process.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 3 Experimental Results
### Two-Step Algorithm Process  
The raw data is directly processed by the two-step processing algorithm and the primary image is shown in Figure 6a. We calculated some parameters of this image, and the result can be found in Table 4, where Na is the number of azimuth samples in the raw echo data, P is the number of azimuth samples after prefiltering, Ba_s is the Doppler bandwidth of the whole imaged scene, Ba_p is the Doppler bandwidth of one target within the imaged scene, and PRF_new is the azimuth sampling rate after prefiltering. Obviously, PRF_new is larger than the Doppler bandwidth (both Ba_s and Ba_p), and as a result, there will not be a Doppler aliasing phenomenon in azimuth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Na** & **P** & **Ba\_p** & **Ba\_s** & **PRF** & **PRF\_new** \\ \hline
32,130 & 38,588 & 16,809.30 Hz & 19,379.69 Hz & 3742.15 Hz & 20,347.89 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Some parameters of GF-3 ST mode data before and after de-chirp.  
Figure 6: One primary image of GF-3 in ST mode. This image is processed by the two-step algorithm directly. (**a**) The whole image of one data take; (**b**) The local part of (**a**) which is a boat moored at the bank of the Yangtze River that we treat as a corner reflector.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Carrier Frequency & 5.4 GHz \\ Pulsewidth & 45 \(\upmu\)s \\ Bandwidth & 240 MHz \\ Sample Frequency & 266.67 MHz \\ Pulse Repeat Frequency (PRF) & 3742.15 Hz \\ Ve 1 & 7123.837734 m/s \\ Azimuth Steering Range & [\(-1.78^{\circ}\), \(1.78^{\circ}\)] 2 \\ Step Steering Angle & 0.01\({}^{\circ}\) \\ Look Angle & 33.75\({}^{\circ}\) \\ Incidence Angle & 38.51\({}^{\circ}\) \\ Synthetic Aperture Time & 8.584 s \\ Range Window & Taylor (\(-24\) dB) \\ Azimuth Window & Taylor (\(-19\) dB) \\ Range Resolution & 0.731 m \\ Azimuth Resolution & 0.545 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some parameters of an image in ST mode.  
To analyze the quality of the focused image produced by our approach, a boat moored at the bank of the Yangtze River, which is shown in Figure 6b, is treated as a corner reflector; some results can be found in Table 5 and Figure 7. The overall improvement of the performance is related mainly to the peak sidelobe ratio (PSLR) and the integrated sidelobe ratio (ISLR), rather than to the resolution. So Taylor weight windows are added in both range (\(-24\) dB) and azimuth (\(-19\) dB). This operation can suppress the sidelobes effectively, but the main lobe will widen (i.e., lose resolution). The theoretical resolution is 0.625 m in range and 0.447 m in azimuth. After the weighting operation, the actual resolution is 0.731 m in range and 0.545 m in azimuth. It should be noted that the azimuth signal is not only weighted by a Taylor window, but also by the antenna pattern. Whereas calculating a precise antenna pattern window is very difficult because of the SNP, the actual azimuth resolution is the result of a simulation. The contour in Figure 7c is mussy.
## 2 Topologies in Smart Inverters
### Topologies under Wireless Communication  
Most common wireless systems are M2M, H2M communication, and Long Term Evolution (LTE) communication. However, the devices that can support these features are small. In addition, M2M communication is sensitive to delays. Extremely short range communication cannot be used for inverters as the range of the microgrid may vary from several km to tens of km. Hence, NFC (near field communication) cannot be used. Short-range active radio frequency systems can be used for shorter distance. Low-power wide area networks (LPWANs) are made suitable to smart inverter application. LoRa is popular due to its low operating cost and larger range.  
ZigBee and LPWAN are well suited for M2M communication. Mesh topology is used in ZigBee systems, which provides advantages over fault tolerance. However, the routing process is complex and provides lower efficiency. Due to the multi-hop mesh structure, the data rate may decrease once the signal reaches the final destination.  
Meanwhile, a network with star topology can be observed with central nodes connected to the internet in LPWANs and SIGFOX. Complexity as seen in ZigBee topology is reduced in LPWANs. At each node, power consumed is lower than that of ZigBee. The main disadvantage with LPWAN is low reliability. If the main or the principal node fails to operate, to keep the network active, other nodes cannot compensate. Figure 4a,b shows the schematic of ZigBee and LoRa. Figure 5 shows the comparison of different wireless topologies as a function of range and data rate.  
Figure 4: (a,b) shows the schematic of ZigBee (Mesh) and of LoRa (Star).  
Figure 5: Comparison of different wireless topologies as a function of distance and speed.
# Conductive Textiles for Signal Sensing and Technical Applications
## 5 Application of Conductive Textiles
### Other E-textiles Applications  
The demand for electronic yarn or e-yarn is increasing day by day. A prototype to produce e-yarn--as can be seen in Figure 10--using the encapsulation process was reported to be incorporated into the fabric via knitting and weaving machinery that can meet the demand for electronic textile material [324].  
Due to their softness, breathability, and biocompatibility, wearable e-textiles have attracted increasing appeal, making them long-lasting and wearable in the long run. A multifunctional integration system with thermal insulation, Joule heating, and particulate matter (PM) filter features was reported using conductive nanowires [325]. Several companies, including Google, which is developing capacitive touching textiles in its Google Jacquard project, have recognized the potential of electronic wearable textiles. This project enables a seamless and dependable wearable computing concept that can assist customers in performing daily tasks, such as answering phone calls without interrupting an ongoing activity [326].
## 5 Design of Front-End Application Layer Function Pages
### Fault Alarm Page  
The fault alarm page includes the display of the core device faults of the monitoring system and the remote control of DTPPF shutdown. The back-end service layer listens to the required parameters for device fault monitoring of the DTPPF via WebSocket. WebSocket calls the saveDeviceData() method in the back-end service layer with the corresponding field in the data and saves the parameters in the power_i, filter_i, power_u, and capa_ui tables. Then, the per-unit values of the core devices are calculated and stored in the DTPPF_faultpara table by calling the saveFaultPara() method in the back-end service layer. Based on these values, any faults in the core devices can be determined. When a fault occurs, the back-end service layer queries the user table to retrieve the user's mobile number. It then uses a third-party WebService interface to push the fault information to the user's mobile phone. The fault information is stored in the DTPPF_faulttype table for display in the front-end application layer. Users can remotely command the DTPPF to stop using the \"stop device\" button on the fault alarm page.
## 3 Identification of Multivariable Hammerstein Model
\begin{array}{ccccc}\theta^{T}=\left[\begin{array}{ccccc}m_{0}&m_{1}&m_{2 }&\cdots&m_{n}\end{array}\right]\in R^{1\times n(1+n_{u}q)},\\ m_{0}^{T}=\left[\begin{array}{ccccc}a_{1}&a_{2}&\cdots&a_{n}\end{array}\right],\\ m_{i}^{T}=\left[\begin{array}{ccccc}c_{11}b_{i1}&c_{12}b_{i2}&\cdots&c_{1n_{u }}b_{in_{u}}&\cdots&c_{q1}b_{i1}&c_{q2}b_{i2}&\cdots&c_{qn_{u}}b_{in_{u}}\end{array} \right]\in R^{1\times n_{u}q},\\ \psi_{t}^{T}=\left[\begin{array}{ccccc}\psi_{t}(0)&\psi_{t}(1)&\psi_{t}(2)& \cdots&\psi_{t}(n)\end{array}\right],\\ \psi_{t}^{T}(0)=\left[\begin{array}{ccccc}-y_{t-1}&-y_{t-2}&\cdots&-y_{t-n} \end{array}\right],\\ \psi_{t}^{T}(i)=\left[\begin{array}{ccccc}u_{t-i}(1)&u_{t-i}(2)&\cdots&u_{t-i }(n_{u})&\cdots&u_{t-i}(1)^{q}&u_{t-i}(2)^{q}&\cdots&u_{t-i}(n_{u})^{q}\end{array} \right],\end{array}  \tag{4}\]  
Then (3) can be rewritten into the following form:  
\[y_{t}=\psi_{t}{}^{T}\theta \tag{5}\]  
and the recursive least squares method is used to identify parameters \(\theta\):  
\[ \begin{array}{c}K_{m}=P_{m-1}\psi_{m}\left[1+\psi_{m}{}^{T}P_{m-1}\psi_{m} \right]^{-1},\\ \hat{\theta}_{m}=\hat{\theta}_{m-1}+K_{m}\left[y_{m}-\psi_{m}{}^{T}\hat{\theta} _{m-1}\right],\\ P_{m}=P_{m-1}-K_{m}\psi_{m}{}^{T}P_{m-1}\end{array}  \tag{6}\]  
where \(m\) represents the current moment, \(\hat{\theta}_{m-1}\) represents the parameter estimation at the previous moment. After obtaining the estimated value \(\hat{\theta}\) of the parameter \(\theta\), the parameter \(a_{i}(i=1,2,\cdots,n)\) can be obtained directly from \(\hat{\theta}\), but the parameter \(b_{i}(i=1,2,\cdots,n)\) also needs to be separated from the parameter \(c_{ij}\). Without loss of generality, let's assume that \(c_{1j}=1,\left(j=1,2,\cdots,n_{u}\right)\), then \(b_{i}(i=1,2,\cdots,n)\) can be obtained directly from the first to \(n_{u}\) lines of \(m_{i}\). After obtaining the parameter \(b_{i}(i=1,2,\cdots,n)\), the remaining parameter \(c_{kj}(j=1,2,\cdots,n_{u},k=2,3,\cdots,q)\) of the nonlinear function is obtained by:  
\[c_{kj}=\frac{1}{n}\sum_{i=1}^{n}\frac{m_{i}((k-1)n_{u}+j,:)}{b_{ij}} \tag{7}\]  
where \(m_{i}((k-1)n_{u}+j,:)\) represents the \([(k-1)n_{u}+j]\)-th row element of \(m_{i}\). In summary, the linear and nonlinear coefficients of the Hammerstein model (2) of the MISO system can be obtained through Equations (6) and (7), and the obtained Hammerstein model of the cooling system of the MGT-CCP will be shown and verified in Section 5.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 2 Theoretical Background
### Asynchronous Programming  
In asynchronous programming, by allowing multiple processes to be executed simultaneously, the overall performance and scalability of the application can be improved significantly. In traditional synchronous programming, there is a linear flow of execution,as a task can only be performed after the previous one is completed. There are various interfaces provided by the Java ecosystem that can be used to develop asynchronous programs: _Future_s and _Executors_[5]. When the result of an asynchronous operation becomes available, it can be accessed in a non-blocking way using a _Future_. A pool of threads is managed by _Executors_ to perform tasks asynchronously while also allowing for more efficient usage of system resources, as the threads managed through these implementations can be reused instead of being created and deleted for each task. Where tasks are I/O-bound or have high latency, the employment of asynchronous programming can significantly increase application performance and scalability. However, it can also present some issues, such as increased complexity in controlling the execution flow and handling concurrency errors [12].
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 1 Introduction
An extension of [12] to the case of multi-antenna receiver was presented in [13], where the test statistic was built from a linear combination of the per-antenna test statistics. Inspired by [12], our approach is to focus on the uplink signal detection and the performance analysis for a communication system that utilizes AmBC over ambient OFDM signals. Our main ideas and contributions are highlighted as follows. First, we introduce the system model for the AmBC over ambient OFDM carriers in the air and the test statistic for tag signal detection, which are established in [12]. Second, we design an improved energy detector by proposing an arbitrary positive power operation on the signal amplitude instead of the squaring operation given as the previous work. Numerical results demonstrate that the proposed detector with optimum power order can achieve lower bit error rate (BER) and higher data rate than those in [12]. The remainder of this paper is organized as follows. Section 2 presents the system model and the problem formulation. Section 3 analyzes the optimal energy detector design for the proposed scheme. Section 4 gives the numerical results, followed by the conclusion in Section 5.