esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology
## 3 Main Results  
### PI Algorithm  
We provide Algorithm 1, a proposed PI algorithm, along with its accompanying stability analysis and convergence proof within this part.  
```
Step 1: Let the iteration index \(s=0\) and initial policies \((u_{1}^{0}(k),\ldots,u_{i}^{0}(k),\ldots,u_{N}^{0}(k))\) be admissible. Step 2: (Policy evaluation) Complete the following equation \[V_{i}^{s}(e_{i}(k))=E\big{[}r(e_{i}(k),u_{i}^{s}(k),u_{-i}^{s}(k))\big{]}+\gamma V _{i}^{s}(e_{i}(k+1)).\] (27) Step 3: (Policy improvement) Revise the control policy using \[u_{i}^{s+1}(k)=\arg\min_{u_{i}(k)}\{E\big{[}r(e_{i}(k),u_{i}(k),u_{-i}(k)) \big{]}+\gamma V_{i}^{s}(e_{i}(k+1))\}.\] (28) Step 4: Let \(s=s+1\). Proceed by returning to Step 2.
```  
**Algorithm 1** PI algorithm  
**Remark 2**.: _Initialization in Step 1 starts with any initial admissible control policy, which means that it does not need to be optimal, but only needs to maintain the normal operation of the system and prevent the system from diverging or being in an unstable state. In practical applications, some simple attempts can be made. For example, in numerical simulation, observe whether \(e_{i}(k)\to 0\) as \(k\to\infty\), whether the control input tends to zero with time, and whether the performance function is finite and reasonable._  
**Remark 3**.: _The idea of Step 3 is to choose the policy that minimizes the value function of the current step, making it closer to the optimal policy. It is a theoretical algorithm, but, in practical applications, it is implemented using NNs. In this paper, the optimal control policy is iteratively estimated using an actor network, with weights adjusted to minimize the error between the desired final cost target and the value function. (See Section 5 for details.)_  
In accordance with (23) and (27), the corresponding formula is as follows:  
\[V_{i}^{s}(e_{i}(k),\tau)=r_{E}^{s}(e_{i}(k),\tau)+\gamma\sum_{\tau^{\prime}}V _{i}^{s}(e_{i}(k+1),\tau^{\prime})\cdot p_{\tau\tau^{\prime}}, \tag{29}\]  
where \(r_{E}^{s}(e_{i}(k),\tau)=E\big{[}r(e_{i}(k),u_{i}^{s}(k),u_{-i}^{s}(k))\big{|} \sigma(k)=\tau\big{]}\).
###### Abstract  
Fault injection attacks pose a serious threat to many cryptographic devices. The security of most cryptographic devices hinges on a key block called modular division (MD) over a prime field. Although a lot of research has been done to implement the MD over a prime field in hardware efficiently, studies on secure architecture against fault injection attack are very few. A few of the studies that focused on secure architecture against fault injection attack can only detect faults but not locate faults. In this regard, this paper designs a novel secure architecture for the MD over a prime field, which can not only detect faults, but also can locate the error processing element. In order to seek the best optimal performance, four word-oriented systolic structures of a main function module (MFM) were designed, and three error detection schemes were developed based on different linear arithmetic codes (LACs). The MFM structures were combined flexibly with the error detection schemes. The time and area overheads of our architecture were analyzed through the implementation in an application-specific integrated circuit (ASIC), while the error detection and location capabilities of our architecture were demonstrated by C++ simulation, in comparison to two existing methods. The results show that our architecture can detect single-bit error (SBE) with 100% accuracy and locate the erroneous processing element (PE), and correctly identify most of the single PE errors and almost all of the multi-PE errors (when there are more than three erroneous PEs). The only weakness of our architecture is the relatively high time and area overhead ratios.  
modular division (MD); secure architecture; fault injection attacks; concurrent error detection; systolic implementation +
Footnote †: journal: Applied  
_Article_  
A Secure Architecture for Modular Division over a Prime Field against Fault Injection Attacks
## 4 Evaluation
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
As metrics for evaluating classification model performance, we used accuracy, precision, and recall [51] defined as follows:  
\[Accuracy =\frac{tp+tn}{tp+tn+fp+fn}, \tag{1}\] \[Precision =\frac{tp}{tp+fp},\] (2) \[Recall =\frac{tp}{tp+fn} \tag{3}\]  
where \(tp\), \(fp\), \(tn\), and \(fn\) are numbers of true-positive, false-positive, true-negative, and false-negative predictions, respectively [51]. For example, \(tp\) indicates the number of malicious samples correctly predicted by a model as malicious.
## 1 Introduction
Specifically, it receives necessary bandwidth and time-width echoes from targets by dechirping and subaperture decomposition in the range and azimuth directions, respectively. Second, to reduce the consumption of computation time, we propose a target-oriented processing module, which only processes the echoes from targets. Specifically, it only obtains the images of targets with a necessary resolution by parallel-streaming inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. Third, to allocate resources reasonably, we propose a decision module, which decides the parameters of the receiving and processing modules. Specifically, it determines the receiving window, the receiving aperture in the range and azimuth directions, and the processing bandwidth by constant false alarm rate (CFAR) detection to extract areas of targets. Fourth, to allocate resources adaptively, we connect three modules in a closed loop to realize feedback. Specifically, it fulfills allocating resources adaptively by imaging and detecting targets progressively from coarse to fine. The contributions of this work are summarized as follows. 1. A target-oriented imaging concept is proposed for HRWS SAR imaging. To the best of our knowledge, this is a first in the HRWS SAR imaging community. 2. An adaptive receiving-processing-decision feedback framework, aiming at specific application fields such as maritime surveillance, is proposed to fulfill the concept. Wepropose three modules (2D adaptive receiving module, target-oriented processing module, and decision module) with a closed-loop connection. The modules and the link are designed considering characteristics related to the application field, imaging scene, and the radar system. 3. The proposed framework simplifies the processing flow, providing a feasible end-to-end way for HRWS SAR online maritime surveillance to obtain the target detection results directly. The feasibility of the proposed target-oriented imaging concept and the realization of the imaging framework is verified through numerical simulations. The system's resources and computation time are significantly saved compared with the current framework. For a typical scenario, our framework saves at least 30% sampling resources, 33% storage resources, and 99% computation time. The rest of this paper is organized as follows. Section 2 reviews the current imaging framework. Section 3 introduces the proposed imaging concept and its realization framework. Section 4 presents the experiments and the results. Conclusions are shown in Section 5.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Approaches to Energy-Aware Security
#### 4.3.5 Machine Learning Methods  
* _Anomaly Detection and Intrusion Prevention_. Supervised learning algorithms can be used to identify types of attacks with high accuracy, whereas unsupervised learning algorithms are able to detect unknown, untagged attacks and anomalous behaviors. [PERSON] et al. [97] demonstrated the outstanding effectiveness of machine learning (ML) models in identifying attacks, particularly in the realm of anomaly detection (binary classification) and anomaly classification (multi-class issues).
* _Real-Time Monitoring_. Based on historical energy usage data, ML models can be trained to identify patterns of energy wastage to facilitate designing energy-aware security policies. Additionally, ML models deployed on IoT devices can monitor incoming data streams to detect security threats in real-time while minimizing power consumption. For example, in IoT systems powered by federated learning algorithms, raw data are kept locally, with only model parameters transmitted to a central server or other devices (see Figure 10). By keeping sensitive data on local devices, federated learning reduces the risk of data leakage or unauthorized access, as well as the energy required to transfer large amounts of data [98].
## 3 Results
### Radiological Hazard Indices  
Figure 5a-d shows the radiological hazard indices, which include the external hazard index (\(\mathrm{H_{ex}}\)), radium equivalent activity (\(\mathrm{Ra_{eq}}\)), absorbed dose rate (\(\mathrm{D_{Rev}}\)), and annual effective dose equivalent (\(\mathrm{AED_{ex}}\)). These indices were calculated using the equations listed in Table 2.  
The external hazard index is an assessment of the hazard of the natural gamma radiation and is used to detect the radiological suitability of a material [14; 19]. The \(\mathrm{H_{ex}}\) values ranged from 0.05 to 0.31, with an average of 0.22 (Figure 5a), all of which are less than the permissible value of unity.  
The radium equivalent activity is widely used as a hazard index, with a formula for comparing the specific activity of materials containing different amounts of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K [14; 19]. It is defined as a single quantity that represents the combined  
Figure 5: Radiological hazard indices of soil samples \(\mathrm{H_{ex}}\) (**a**), \(\mathrm{Ra_{eq}}\) (**b**), \(\mathrm{D_{Rev}}\) (**c**), and \(\mathrm{AED_{ex}}\) (**d**), with 'PS’ indicating the present study.  
specific activities of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K, and provides a numerical indicator of an external dose to the public [19]. The Ra\({}_{\text{eq}}\) values varied from 17.5 Bq/kg to 116.3 Bq/kg, with a mean of 83.0 Bq/kg (Figure 5b). These values are lower than the recommended limit of 370 Bq/kg [19].  
The outdoor absorbed dose rate ranged from 8.3 nGy/h to 54.4 nGy/h, with an average value of 39.0 nGy/h. The calculated annual effective dose equivalent due to gamma radiation ranged from 10.1 \(\mu\)Sv/y to 66.7 \(\mu\)Sv/y, with a mean of 47.8 \(\mu\)Sv/y. All D\({}_{\text{Rex}}\) and AED\({}_{\text{ex}}\) values are also lower than the global population weight average of 59 nGy/h and 70 \(\mu\)Sv/y, respectively [14]. Samples S14, S15, and S29 had much lower radiological hazard indices due to their low radioactivity.  
The radiological risk values in the present study compared to soils around NPPs and nuclear facilities in other countries are listed in Table 7. The values from the present study are lower than those reported by previous studies across Taiwan [11; 16], Thailand [17], and India [18] and are similar to those reported from Korea [13]. The radiological hazard indices H\({}_{\text{ex}}\), Ra\({}_{\text{eq}}\), D\({}_{\text{Rex}}\), and AED\({}_{\text{ex}}\) values indicate that the area does not pose a significant radiological health risk. Therefore, the naturally occurring radionuclides in the soil around the Maanshan NPP do not pose a significant radiological risk to residents and tourists. The data obtained in this study provide useful information on the background radioactivity of the study area.
## Materials and Methods
### Remote Sensing Datasets
#### 2.2.3 Auxiliary Datasets  
The auxiliary datasets that were used in the study included soil texture, temperature, elevation, normalized difference vegetation index (NDVI), and land-cover classification datasets. The soil texture information was obtained from the Harmonized World Soil Database (HWSD [45]) provided by the Food and Agriculture Organization (FAO), which is a 30 arc-second raster database with over 15,000 different soil mapping units that combines existing regional and national updates of soil information worldwide. The bulk density and the fraction of clay and sand were extracted from the database for the experiments, and registered to the Sentinel-1A images with a resolution of 1 km. Temperature, NDVI, and land-cover classification information of the study site were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products. The MODIS products were the Terra surface temperature product of 1 km resolution, the Terra 16-day composite NDVI product of 250 m resolution, and the combined International Geosphere Biosphere Programme (IGBP) land-cover product of 500 m resolution. The Shuttle Radar Topography Mission (SRTM) [46] 90 m digital elevation data were used to acquire the elevation information. All of the above auxiliary data were resampled and registered to the Sentinel-1A images with a resolution of 1 km.  
To help with the understanding of the following spatiotemporal analysis, statistical metrics, and spatial distribution of the soil texture and elevation are shown in Table 1 and Figure 2, respectively. The region is dominated by sandy soil, for which the maximum fraction of sand reaches as high as 94%. The fraction of clay in the soil is higher in the southeast and northeast. The terrain is relatively flat with an average elevation of 46.2 m and a standard deviation of 12.9 m in the study site.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Min** & **Max** & **Mean** & **SD** \\ \hline Clay & 4.0\% & 63.0\% & 13.2\% & 11.3\% \\ Sand & 11.0\% & 94.0\% & 74.9\% & 20.4\% \\ Elevation & 1.0 m & 85.0 m & 46.2 m & 12.9 m \\ \hline \end{tabular}
\end{table}
Table 1: The minimum (Min), maximum (Max), average (Mean), and standard deviation (SD) of the elevation and soil texture in the study area.  
Figure 2: Soil texture and DEM information of the Dahra study site: (**a**) the false-color map of soil texture taking the sandy portion as red, the silty portion as green and the clayey portion as blue; (**b**) Shuttle Radar Topography Mission (SRTM) DEM map.
# Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT
## 2 Material and Methods
### Study Site Description and Design
In the second phase, before the hardening of the primary grout, global pressure of 1.2 MPa is applied using a preinstalled sleeved grout pipe with a pressure of 1.3 MPa. Figure 2 shows the excavated micropile tube element after the first phase of the pressure grout. Type II cement according to the ASTM C150/AASHTO M85 with the design compressive strength of 32 MPa was used in the grout material. The water/cement ratio of the grout mix was 0.45. Chemically compatible additives were used in the grout material to enhance the grouting efficiency in long distances from the core of micropile elements, and to improve the pumping range. The Type C post-grouting method is usually expected to inject the grout up to the rate of 50 L/min, per the FHWA-NHI guideline [23]. At sites I and II, the grouting rate was recorded between 260 to  
Figure 1: Research study sites [45]. 290 L/min. This considerably higher range of grout delivery was almost five to six times the expected rate and shows the porosity of the soil profile in the project, which mainly consisted of SP clean sands. The micropile elements were spaced in a \(2.5\times 2.5\) m center-to-center grid. At site I, CPT measurements were carried out prior to and after performing type C-C1 jet grouting using micropiles. The CPT values before and after micropile installation were measured in a single boring at the same location, based on logging the data of five boreholes, (BH-I-I,5). The risk of liquefaction triggering is evaluated by analyzing the measured CPT values, such as cone penetration resistance (\(\text{q}_{\text{c1 Ncs}}\)) and sleeve resistance (\(\text{f}_{\text{s}}\)). In order to enhance the precision and accuracy of outcomes in this research, the CPT-based simplified liquefaction triggering was assessed following the specifications and recommendations of [PERSON] 2014 Boulanger [PERSON] 2014 and Eurocode 8 (2014), along with the Juang 2006 (2014) method. The peak ground acceleration at the ground level of site I was measured as \(\text{a}_{\text{max}}=0.35\) g. The calculations were performed based on the moment magnitude \(\text{M}_{\text{W}}=7.5\). According to the bore log data at the site I, the groundwater level (GWL) during the CPT test was 1.2 m. The GWL during the earthquake was assumed to be 1.7 m.  
At site II, SPT was performed before and after jet grouting by micropiles. The soil liquefaction potential was evaluated by using \(\left(N_{1}\right)_{60\text{s}}\) values. To shed further light on the accuracy of the analyses, specifications and recommendations by [PERSON] and [PERSON] 2014, NCEER (1997), and Eurocode 8 were followed for the SPT-based liquefaction assessment. In the calculation procedure, \(\text{a}_{\text{max}}=0.35\) g and \(\text{M}_{\text{W}}=7.5\) values were used. According to the bore log data at site II, the groundwater level (GWL) during the CPT test was 1.5 m. The GWL during the seismic loading was assumed to be 2.0 m.
## 3 Indoor Positioning Systems
### Systems Based on Light
#### 3.4.2 Nonvisible Light  
The best known and most used approach is infrared (IR) [60]. A simple infrared system consists of a light-emitting diode, which emits infrared signals as bursts of nonvisible light, and a receiver photodiode to detect and capture light pulses, which are then processed to retrieve information [105]. The reliability of the IR system is affected by many characteristics of the emitted optical signal, e.g., its directivity or to what degree it is unidirectional. Another important aspect is the way it reacts to obstacles, such as reflection and dispersion (irregularities in reaching obstacles). Most infrared systems require distance from the emitter's line of sight (LOS) to the sensor, although sometimes the reflected signals have enough energy to activate the sensor. Obviously, in the context of IR IPS systems, the lack of a direct view between the sender and receiver is a significant disadvantage, as the signal suffers from occlusion between the two devices [5].  
A system developed by [PERSON] et al. estimated the position of a target using a combination of RSS measurements in data networks, either fixed in the scenario or mobile, in robots [106]. The system arranged together with the user issues a unique infrared code every 10 s. The infrared sensors, which are placed around the office environment, capture the emitted codes. The information received by the sensor network is processed and made available on portable devices. The system has two limitations: it requires LOS between the receivers and the emblem, and the performance of the system is affected by sunlight. This system has been reported to compromise user privacy. During implementation, some users answered a questionnaire and were extremely uncomfortable to know that their locations were shared with other users [106].  
[PERSON] et al. developed a study that aimed to estimate the location of a mobile robot due to its active configuration [107]. To assess the position of the target, the authors observed the measured distances between the destination and the predetermined reference points, using a system of non-linear equations of hyperbolic trilateration. The accuracy noted by the authors during the expressions was about 0.10 m.
## 3 System Design
### Transmission Mechanism
#### 3.2.1 Design and Features of Integrated SRT Protocol  
The SRT design includes optimized connection establishment and handshake procedures which enhanced stability and reliability of network connection. It utilizes a lightweight four-way handshake mechanism (INDUCTION-CONCLUSION). Compared with TCP's three-way handshake, SRT dedicates its first phase to negotiating crucial transmission parameters such as MTU, latency tolerance, and encryption configuration, with the second phase completing connection confirmation. This entire process is fully optimized for real-time transmission. Within our chain-based architecture, each node's listener-caller mode enables it to swiftly establish a new connection with a fallback node upon detecting an upstream link failure. This connection establishment typically falls within 50-100 ms, significantly faster than the multi-second reconnection times often seen in traditional P2P systems.  
Furthermore, SRT incorporates a sophisticated bidirectional adaptive flow control mechanism. This intelligent stream control is driven by receiver-side feedback, continuously monitoring receive buffer occupancy, network RTT variations, and packet loss patterns to dynamically adjust the sender's data transmission rate. Specifically, the receiver periodically sends ACK packets within a defined time window, carrying information about its current buffer status and network quality assessment. The sender then uses this feedback to calculate an optimal sending window, formulated as  
\[SendingRate_{optimal}=\min\left(\frac{BufferSpace}{RTT},BandwidthEst\times(1- LossRate)\right) \tag{2}\]  
where \(BufferSpace\) denotes the buffer space available at the receiver and \(BandwidthEst\) represents the estimated bandwidth. This mechanism is particularly vital in multi-hop chain transmission, effectively preventing sudden traffic bursts from upstream nodes from overwhelming downstream node buffers.  
SRT also introduces the concept of latency tolerance, allowing the system to wait for retransmission of lost data packets within a specified time window. Packets are discarded if this timeout is exceeded, ensuring real-time integrity. Our system adopts a hierarchical latency configuration strategy, adapting to link hop count and geographical distribution features:  
\[Latency_{hop_{i}}=BaseLatency\times(1+DistanceFactor_{i}+HopPenalty_{i}) \tag{3}\]  
In this equation, \(BaseLatency\) signifies a fundamental latency setting (typically 120-200 ms), \(DistanceFactor_{i}\) is a geographical distance correction factor, and \(HopPenalty_{i}\) accounts for multi-hop cumulative penalty. This precise latency control enables the system to minimize end-to-end delay while ensuring smooth video playback.