esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 3 Proposed Partial Decoding of LDPC Codes
### Decoding of a Partial H-Matrix  
The message is encoded with the full H-matrix of the LDPC code, while the received codeword is decoded by using a partial H-matrix in the partial decoding. Iterative decoding algorithms such as the SPA or MSA can be used to update variable nodes and check nodes based on the partial H-matrix. Before starting a decoding iteration using the partial H-matrix, the syndromes of the updated codeword are checked with respect to the full H-matrix. If the syndromes are all zeros, then the codeword is correct so that the decoding process is finished. Otherwise, we repeat the decoding iteration until we reach the number of maximally allowed iterations (MAI). The detailed procedure of the partial decoding is described in Algorithm 1.  
```
1:Initialization: load the initial LLR values to each variable node.
2:Iterative Decoding: Perform the following steps in accordance with the SPA or MSA.
3:for\(i=0\) to \(i=\mathrm{MAI}-1\)do\(\triangleright\) Iterative decoding
4:for all check nodes included in the full \(\mathbf{H}\),do\(\triangleright\) Syndrome check
5: compute syndrome
6:endfor
7:if syndromes are all zerosthen\(\triangleright\) Decoding success
8: report a decoding successdo terminate the decoding
9:endif
10:for check nodes included in the partial \(\mathbf{H}\),do\(\triangleright\) Check node update
11: update check nodes and generate check-to-variable node (C2V) messages
12:endfor
13:for all variable nodesdo\(\triangleright\) Variable node update
14: update LLR values and generate variable-to-check node (V2C) messages
15:endfor
16:endfor
17:report a decoding failure\(\triangleright\) Decoding failure
```  
**Algorithm 1** Partial LDPC decoding.  
The error-correcting capability resulting from a partial H-matrix is investigated based on a (149, 61, 6) array LDPC code that is designed to protect a message of 1 KB. The SPA is employed to decode the received codeword with setting MAI to 30. Figure 1 shows how the error-correcting capability changes over the channel SNR. The uncorrected bit-error rate (BER) performances resulting from \(\mathbf{H}_{2}\) and \(\mathbf{H}_{6}\) correspond to the weakest and strongest error-correcting capabilities, respectively. The decoding strength is stronger when the partial H-matrix becomes larger. Therefore, it is possible to support diverse error-correcting capabilities by constructing several partial H-matrices from a single H-matrix. Though \(\mathbf{H}_{2}\) shows the weakest decoding strength, it removes two thirds of memory accesses compared to the full H-matrix. This enables a tradeoff between decoding capability and energy consumption, since the number of memory accesses dominates the energy consumption of an LDPC decoder [13].
## 2 Materials and Methods
### Empirical Safety Stock Estimation
#### 2.2.3 Kernel Density Estimation (KDE)  
The Kernel density estimation (KDE) method expressed the probability density function f (x) of forecast errors during the lead time. It does not provide any assumption regarding the form of data distribution. On every data point xi, we place a kernel smoothing function K. The KDE is expressed in Equation (12).  
\[\hat{\mathrm{f}}(\mathrm{x})=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}} \mathrm{K(x-x_{i})} \tag{12}\]  
We use h to control the bandwidth of \(\hat{\mathrm{f}}(\mathrm{x})\) by writing Equation (13) as proposed by [PERSON] [16].  
\[\hat{\mathrm{f}}(\mathrm{x})=\frac{1}{\mathrm{Nh}}\sum_{i=1}^{\mathrm{N}} \mathrm{K}\bigg{(}\frac{\mathrm{x-x_{i}}}{\mathrm{h}}\bigg{)} \tag{13}\]  
Where N is the sample size. The kernel function K is typically: Everywhere non-negative: \(\mathrm{K(x)}\geq 0\) for every x, Symmetric: \(\mathrm{K(x)}=\mathrm{K(-x)}\) for every x and Decreasing: \(\mathrm{K^{\prime}(x)}\leq 0\) for every x \(>0\).  
The Epanechnikov kernel, which means optimal kernel function, is given by Equation (14).  
\[K_{\epsilon}(\mathrm{t})=\bigg{\{}\begin{aligned} \frac{3}{4\sqrt{5}}\Big{(}1- \frac{\mathrm{t}^{2}}{5}\Big{)},&\text{if}-\sqrt{5}\leq \mathrm{t}\leq\sqrt{5}\\ 0,&\text{E}\end{aligned} \tag{14}\]For a Gaussian kernel, the following optimal bandwidth [39] is usually chosen as shown in Equation (15).  
\[\mathrm{h}_{\mathrm{opt}}=0.9\;\mathrm{AN}^{-(1/5)} \tag{15}\]  
where A is an adaptive estimate of the spread that is computed using Equation (16).  
\[\mathrm{A}=\mathrm{min}\;(\mathrm{standard}\;\mathrm{deviation},\mathrm{ interquantile}\;\mathrm{range}/1.34) \tag{16}\]  
The empirical quantile Q(CSL) is estimated basis on the empirical distribution fitted by the kernel approach on the forecast errors during lead time.
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 5 Seismic Fragility Analysis of the TTLS with Foundation Deformation
### Foundation Horizontal Displacement Analysis  
Due to geological conditions or external loads, foundations can experience not only settlement but horizontal displacement as well. The seismic fragility of the TTLS when supports A and B have 0.25 m horizontal displacement in the +Y direction is analyzed in this section. Figure 19 shows the IDA curves of the RM and the JM. The maximum tower top displacement for the RM ranges from 0.79 m to 1.97 m, whereas the maximum tower top displacement for the JM ranges from 1.45 m to 3.5 m. The displacement range for the JM is approximately 1.59 to 1.81 times larger than that for the RM. Subsequently, the results are fitted through Equation (2), as shown in Figure 20.  
Figure 17: Seismic fragility curves of the TTLS with foundation settlement.  
Figure 18: _ISDR_ of transmission tower: (**a**) RM; (**b**) JM.  
The seismic fragility curves of the RM and the JM subjected to foundation horizontal displacement are shown in Figure 21. Consistent with the foundation settlement, when the horizontal displacement of the foundation occurs, the seismic fragility curves of the JM shift to the right, indicating that ignoring the bolt slippage behavior would overestimate the seismic failure probability of TTLSs.  
Figure 21: Seismic fragility curves.  
Figure 19: IDA curves of TTLSs: (**a**) RM; (**b**) JM.  
Figure 20: Data fitting curves: (a) RM; (b) JM.  
Figure 22 presents the _ISDR_ values of each layer for the RM and the JM. The maximum _ISDR_ values of the two models, subjected to foundation horizontal displacement, are located at section B2 of the tower body. The reason for this phenomenon is that the horizontal displacement of supports A and B leads to the buckling of the diagonal members in the second layer, resulting in an increase in the _ISDR_.  
In summary, the foundation deformation not only increases the seismic failure probability of the TTLSs but also changes the weak position of the transmission tower.
## 5 Implementation
### Recognition and Tracking Module
#### 5.3.2. Blobs Detection  
The proposed system includes two methods for processing the binary images for detecting blobs: _simple blob detector_ (SBD) and _blob detection based on bounding boxes_ (BBBD). SBD is a basic implementation of a blob extractor provided by OpenCV [36]. BBBD is a specific method implemented as part of the reported research. It operates in two phases: the first phase consists of detecting the contour of elements and the second phase performs a search of the minimum rectangles that contains the detected contours (the _bounding boxes_). Both methods return a set of rectangles that contains the blobs detected.
## 2 Methods  
In order to improve the accuracy of the model and the simulation results, we first carried out the model calibration using the reported results [25]. To save on calculation time, half of the device was established for calculation based on the symmetry of the structure. The device structure and some parameters are shown in Figure 1a. Table 1 also shows the geometrical parameters of the simulated device. As shown in Figure 1a, L\({}_{\rm ap}\) was 10 um. To make the CBL form a certain high-resistance region, the region was set as p-type Ga\({}_{2}\)O\({}_{3}\) region in the simulation, which was a virtual pn homojunction and was adopted in other reports [26]. The simulation result is shown in Figure 1b,c. When the gate voltage was 0 V, the breakdown voltage was 263 V (when the current exceeded 1 A/cm\({}^{2}\), the corresponding voltage was the breakdown voltage); when the gate voltage was 6 V and the drain voltage was 20 V, the current density was 0.028 kA/cm\({}^{2}\). In all of the above results, the output and transfer characteristics curves and breakdown voltage of the simulation model are consistent with those reported in another study [25], which verifies the accuracy and feasibility of the model and simulation result. Based on the verified model, p-SnO and other p-type metal oxides as CBLs were included. How the thickness of p-SnO CBL and its doping concentration affect the breakdown voltage and V\({}_{\rm th}\) of the device were mainly studied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline L\({}_{\rm CS}\) & distance between the gate and source & 5 um \\ L\({}_{\rm src}\) & source contact length & 2 um \\ L\({}_{\rm go}\) & overlapping length of the CBL and gate & 7.5 um \\ L\({}_{\rm nh}\) & length of the highly doped channel region & 10 um \\ L\({}_{\rm nl}\) & length of the lowly doped channel region & 25 um \\ L\({}_{\rm ap}\) & current aperture width & 10 um \\ T\({}_{\rm sh,O_{3}}\) & Al\({}_{2}\)O\({}_{3}\) thickness & 0.05 um \\ T\({}_{\rm ch}\) & channel thickness & 0.1 um \\ T\({}_{\rm CBL}\) & current barrier layer thickness & 0.8 μm \\ T\({}_{\rm drift}\) & drift layer thickness & 8.2 μm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the simulated device.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 5 Experimental Results for Aerial Surveillance Application
### Extensive Analysis of the Target Detection Performance
Finally, the estimates of the false alarm rates are shown in the last column of the table in order to compare the capability to control the number of false alarms (the worst case margin of error has been evaluated to be smaller than 2.6% of the estimated value with a confidence coefficient of 0.95). First of all we observe that, consistently  
Figure 7: Detection results over the range-velocity plane for Data Set #1 using two different MF detection schemes: (**a**) DEC-MF-2/4; (**b**) CEN-MF-4. with Figures 6 and 7, the estimated false alarm rates for the case of a MF operation are smaller and closer to the nominal \(P_{\beta\mu}\) value. This especially applies to the decentralized integration strategy and to the centralized detection scheme operating with \(N=3\) or \(N=4\) DVB-T channels. This is likely to be due to the possibility to mitigate the disturbance background by averaging over the results obtained at different frequency channels. In contrast, when exploiting SF channels the actual false alarm rate can increase even by an order of magnitude. Similarly, the detection capability is significantly enhanced when jointly exploiting multiple channels both with a centralized and with a decentralized detection strategy. Specifically, the centralized approach using all the available channels (CEN-MF-4) yields the best performance: it allows to increase the number of correct detections by 27% with respect to the best performing channel and it approximately doubles this number with respect to the worst performing channel. However, it is worth mentioning that the detection improvement of the CEN-MF-4 with respect to the CEN-MF-3 is extremely limited since one of the channels included in the MF integration provides a negligible contribution to the final performance. Therefore, in this case, the exploitation of just three of the available channels would be recommended in order to reduce the system complexity and the computational load. Incidentally, we observe that the selection of good performing channels to be exploited by a multi-frequency detection scheme represents a key-point. In fact the inclusion of bad channels would be paid in term of increased system complexity without providing any improvement in the final performance. However an a priori selection could only be based on average performance (if a pre-screening of the available channels can be performed) or on typical parameters such as the transmitted power, the presence of co-channel and adjacent-channel interference, the transmission mode, etc. Some selection criteria along this line were defined in [23] for the FM radio case and could be successfully applied also to the DVB-T based passive radar case. Obviously, the a priori selection of good channels is not effective against the time varying behavior of each channel and this basically represents the main motivation for the proposed multi-frequency approaches.
## 4 Simulation and Results
### Testing ML Algorithm
In addition to atypical vehicle behavior, some introduced distortions are caused by the specifics of the phase shifters used in the simulation. Thus, the phase value at the input of the emitters calculated by the ML (Table 1) must be rounded up to a discrete value, which the phase shifter can output. We can propose several solutions to this problem. The first is to use planar PAA for more accurate emitter phase control, the second, to switch a beamforming circuit to analog phase shifters.
## 4 A Routing Protocol for LLNs (RPL)
### RPL Objective Functions for Non-Malicious LLNs
#### 4.2.1 Single Metric Method
The experimental results show that RSSI-based IPv6 reduces both FER and transmission time, thus improving the transmission performance. [PERSON] et al. [94] proposed ELITE that introduces a new routing metric known as Strobe per Packet Ratio (SPR). SPR measures the number of transmitted strokes per packet in the MAC layer. ELITE prioritises paths that minimise stroke transmissions, thereby reducing overall transmission activity from source nodes to their destinations. Simulation results from ELITE indicate up to 25% reduction in average SPR and up to 39% improvement in node energy consumption compared to MRHOF [85]. However, [92; 93; 94] are based on link quality metrics, which only capture routing reliability issues as caused by channel errors or delays and do not address other routing reliability issues related to the node metric, such as the node's energy.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 3 CDL: Cooperative Downlink Listening
### Basic Algorithm of CDL  
LoRa end device can send data uplink to the gateway using various channels as shown in Table 1. In addition, as shown in Figure 4, RX1 and RX2 windows followed by uplink data transmission are used for downlink. In general, the RX1 window is used for ACK frame with respect to uplink transmission, and the channel and data rate of RX1 are the same as those used in uplink transmission. Unlike the RX1 window, the RX2 window can use different channel and data rate regardless of uplink  
Figure 6: DevAddr block partition.  
transmission. Here, the key ideas of CDL are to utilize the new DevAddr system in which types of sensors and regional information are represented, and to enable broadcasting using the RX2 window. To achieve this, network server determines a fixed channel out of channels available in BS or Gateway, to be used for the RX2 window, and sends broadcast message using the RX2 window. We also define DownLink (DL) Initiator, which is responsible that all the nodes in the network wake up and listen to broadcast message of network server via gateway at its RX2 window by transmitting a downlink request to the network server periodically.  
The network manager configures the Group Addr field with the area information and type of sensors. This can be preconfigured in factory or configured in the field using a specific device, such as installer, which can configure each device by establishing a connection to an end device using UART or wirelessly. After installation and deployment, each device starts the join process by sending 'join' request message to a nearby BS (or Gateway) based on DevEUI, AppEUI, AppKey. RFU bit in the 'join' request frame is set to '001', which is to be notified of the CDL device, and its Group Addr information is included in MAC Payload. The BS, which listens to the 'join' request frame of a device, forwards it to network server, and the network server accepts the device to join its network. At that time, if the MType field is '000' (join request) and RFU field is '001' (CDL device) in the received MAC frame header, network server stores Group Addr information in the frame payload, and assigns DevAddr based on the Group Addr information received. A 'join accept' message is sent to the requestor, with DevAddr including the newly assigned NwkAddr (12 bits), NwkId, and Group Addr information.  
After completion of the join process, irrespective of sensor type or its region, a DL initiator sends a downlink request message to the network server periodically. On the reception of downlink request message, the network server transmits a broadcast message using the RX2 window. The broadcast message of the network server may be time synchronization or instant query from the application server. Other devices wake up at every period and then listen to broadcast message of network server, more specifically BS, switching its channel to the RX2 channel. Here, downlink messages can be delivered to all the nodes or selective nodes through Group Addr information.
# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 2 Materials and Methods
### Model Building Process  
The models were developed concurrently by two analysts using identical input data and assumptions. The CH model was constructed using formulas embedded in Excel cells. In contrast, the PL model utilized Visual Basic for Applications (VBA--Excel macros) programming language (on top of the Excel formulas) to process patients through the model and to record patient histories. PL simulation outcomes were transferred to separate Excel sheets for tracking patient pathways and for easy comparison with CH results. Initially, both analysts collaborated on the concept, content, and format of the input data sheet. During development, regular cross-validation checks were conducted to compare and verify the models (see below). The analyses for both models were run with the same hardware: Intel core I/3770 processor (Intel Corporation, Leixlip, Ireland), 16 GB RAM, no dedicated GPU, and MS-Office 2010 64-bit.  
It is important to note that the focus of this paper is on comparing PL and CH modeling techniques, not the efficiency of specific programming languages. To ensure consistency and minimize variability, we deliberately constructed both models within the same software (Excel with VBA, MS-Office 2010 64-bit, version 14.0.7235.5000). While programming languages like R and Python could potentially enhance the efficiency of the PL simulation by managing data entirely in memory and bypassing Excel's visual interface overhead, our intention was to create a hybrid model. In this hybrid approach, Excel manages the core calculations, and VBA serves as the engine for simulating patient pathways. This is fundamentally different from using R or Python, where both data management and calculations take place outside the Excel environment. Regarding the efficiency of our VBA code, we adopted best practices to optimize performance, such as disabling screen updates and automatic calculations, ensuring that the simulation ran as efficiently as possible within the Excel-VBA environment.  
It is also worth noting that models can be categorized into three types: (i) 100% Excel-based, where all calculations are performed using Excel formulas; (ii) 100% programming language-based (e.g., R or Python), where calculations and data management occur entirely within the programming language, relying on random access memory; and (iii) hybrid (Excel + VBA), which combines Excel formulas for core calculations with VBA for simulations, as we employed here. To fairly compare the efficiency of VBA with languages like R and Python, one would need to construct a model using pure VBA (without relying on Excel's calculations) and directly compare it to models fully written in R or Python. This would provide a more accurate assessment of each language's efficiency in handling PL simulations. In conclusion, while switching to R or Python might improve computational efficiency, it would shift the model into a fundamentally different category, which is outside the scope of this paper's objective.
## 5 Challenges and Open Directions
### Enhancing Robustness Against Adversarial Attacks  
The robustness of watermarking in latent diffusion models against adversarial attacks remains a significant challenge. Research shows that latent diffusion watermarking methods, such as those embedded during the diffusion process, are susceptible to attacks like latent noise manipulation, variational autoencoder-based reconstructions, and advanced adversarial perturbations [21]. Methods such as [21; 37] are advancing towards robustness by embedding watermarks within latent representations in a way that maintains resistance to various attack vectors. Furthermore, adversarial examples such as those discussed in [50; 51] demonstrate how adversaries might exploit the weaknesses of watermarking methods. Future work should focus on developing advanced training techniques that involve adversarial training specific to the generative processes of diffusion models, improving the resilience of watermarks embedded in both latent and pixel domains.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 4 Estimation of Forwarder Search Delays
### System Model and Notations  
Consider a node that route data to the first available forwarder. This forwarder is the first node that is closer to the destination. As in (47; 48) (see their Section 3.3.1), the wake up time and the position of the forwarders are independent. However, a difference is that we will consider an asymptotic model where the destination is assumed to be at infinity, the network density is uniform, and the network infinite. We assume that the radio range is normalized and is considered to be equal to 1. We consider one step: a node at position \((0,0)\) and the destination at \(x=+\infty,y=0\).