esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 3 Method
### Line Buffer Access Pattern and Control Instruction
#### 3.3.2 Control Code Generation
In the loop iteration, the variables are calculated sequentially, and they are corresponding to the value of each instruction section for Line0. Then, the values of five sections are assembled into the Instruction0 and appended to the instruction list. Following the Line0, the line-initial block of Line1 starts from BRAM0 of LB1 with a inner-BRAM offset of 4 pixels, which can be translated to MEMstart\(=0\) and MEMoffset\(=4\) in Instruction1, respectively. The other sections are conducted using the same manner as Instruction0. In particular, only 2 blocks are required to fulfill Line3 (CYCLE in Instruction3\(=2\)), because there are 12 remainder pixels contained in the last block of Line2. The Line3 does not contain remainder pixels, that results in pixel blocks from Blk11 which perform the same storage pattern with that of blocks from Blk0. Therefore, in the algorithm loop iteration for Instruction3, variable RETURN is set to 1, and the loop stops. Then, the algorithm outputs the instruction list (Instruction0-3) that can be periodically executed in processing continuous image lines. ```
Input: Application parameters: \(N_{line}\), \(N_{blk}\), Hardware information: \(W_{bran}\) Output: Instruction code: Inst Inst\(=\emptyset\); MEMstart = 0; MEMoffset = 0; REMAIN = 0; RETURN = 0; whileRETURN\(==0\)do MEMstart = \(\lfloor\)REMAIN\(/\left(W_{bran}/8\right)\rfloor\); MEMoffset =REMAIN\(\%\left(W_{bran}/8\right)\); CYCLE=\(\lceil\left(N_{line}-\text{REMAIN}\right)/N_{blk}\rceil\); REMAIN=\(N_{blk}-(N_{line}-\text{REMAIN})\%N_{blk}\); ifREMAIN=0 then RETURN=1;  end if Inst.append(Assemble(MEMstart,MEMoffset,REMAIN,CYCLE,RETURN)); end returnInst
```  
**Algorithm 1**Instruction Generation Algorithm in D-SWIM Streaming Architecture \begin{table}
\begin{tabular}{c c c} \hline \hline
**Section** & **Bit-Length** & **Description** \\ \hline MEMstart & \(\lceil\log_{2}N_{bran}\rceil\) & Start BRAM index of line-initial block \\ MEMoffset & \(\lceil\log_{2}W_{bran}\rceil\) & Start position (inside BRAM) of line-initial block \\ REMAIN & \(\lceil\log_{2}N_{blk}\rceil\) & \(N_{remain}\) of the current image line \\ CYCLE & \(\lceil\log_{2}\lceil N_{line-max}/N_{blk}\rceil\rceil\) & Number of blocks in the current image line \\ RETURN & 1 & Flag to reset the instruction-fetch address \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sections of the customized instruction for D-SWIM architecture.
# The G-Convexity and the G-Centroids of Composite Graphs
## 3 Graph Compositions
### Cartesian Product
We show that \(C_{g}(G_{1})\times C_{g}(G_{2})\subseteq C_{g}(G)\) and the equality holds if \(g_{1}n_{2}=g_{2}n_{1}\). **Proposition 22**. : _Let \(G_{1}\) and \(G_{2}\) be two connected graphs. Let \(G=G_{1}\otimes G_{2}\). Let \(g_{1}=gc(G_{1})\); \(g_{2}=gc(G_{2})\); \(n_{1}=\mid V(G_{1})\mid\) and \(n_{2}=\mid V(G_{2})\mid\). If \(g_{1}n_{2}=g_{2}n_{1}\), then \(C_{g}(G)=C_{g}(G_{1})\times C_{g}(G_{2})\)._  
Proof. : Let \((u,v)\in C_{g}(G_{1})\)x\(C_{g}(G_{2})\). From Proposition 21, \(w(u,v)=\max\{g_{1}n_{2},g_{2}n_{1}\}\). Let \((x,y)\
otin V(G)-C_{g}(G_{1})\times C_{g}(G_{2})\). Then, either \(x\
otin C_{g}(G_{1})\) or \(y\
otin C_{g}(G_{2})\) or both. In the first case \(w(x,y)\geq\mid S_{x}\times V(G_{2})\mid>g_{1}n_{2}\). (as \(\mid S_{x}\mid>g_{1}\)). Similarly, in all the other cases, we can show that \(w(x,y)>w(u,v)\). Therefore, \(C_{g}(G)=C_{g}(G_{1})\times C_{g}(G_{2})\). **Remark 3**. : _The converse of this proposition is not true. For, consider \(G=K_{2}\otimes P_{3}\). Here, \(g_{1}=1\), \(n_{1}=2\), \(g_{2}=1\), and \(n_{2}=3\). Clearly \(g_{1}n_{2}\
eq g_{2}n_{1}\) but \(C_{g}(G)=C_{g}(K_{2})\times C_{g}(P_{3})\)._  
**Proposition 23**. : _Let \(G_{1}\) and \(G_{2}\) be two connected graphs. Let \(G=G_{1}\otimes G_{2}\). Let \(g_{1}=gc(G_{1})\); \(g_{2}=gc(G_{2})\); \(n_{1}=\mid V(G_{1})\mid\) and \(n_{2}=\mid V(G_{2})\mid\)._  
_If \(g_{1}n_{2}\
eq g_{2}n_{1}\), then \(C_{g}(G_{1})\times C_{g}(G_{2})\subseteq C_{g}(G)\)._  
Proof. : Let \((u,v)\in C_{g}(G_{1})\)x\(C_{g}(G_{2})\). From Proposition 10, \(w(u,v)=\max\{g_{1}n_{2},g_{2}n_{1}\}\). Without loss of generality, assume that \(w(u,v)=g_{1}n_{2}\). Let \((x,y)\in V(G)\). if \(x\
otin C_{g}(G_{1})\) and \(y\
otin C_{g}(G_{2})\), then \(w(x,y)>g_{1}n_{2}\) (as \(\mid S_{x}\mid>g_{1}\) and \(S_{x}xV(G_{2})\) is a g-convex set of \(G\) not containing \((x,y)\)). If \(x\
otin C_{g}(G_{1})\) and \(y\in C_{g}(G_{2})\), then \(w(x,y)\geq\mid S_{x}xV(G_{2})\mid>g_{1}n_{2}\) (as \(g_{1}<\mid S_{x}\)). Therefore in this case \((x,y)\
otin C_{g}(G)\). If \(x\in C_{g}(G_{1})\), then \(w(x,y)=\max\{g_{1}n_{2},w(y)xn_{1}\}\geq g_{1}n_{2}=w(u,v)\). Thus for all \((x,y)\), \(w(x,y)\geq w(u,v)\) for every \(u\in C_{g}(G_{1})\) and \(v\in C_{g}(G_{2})\). This proves that \(C_{g}(G_{1})\)x\(C_{g}(G_{2})\subseteq C_{g}(G)\). **Remark 4**. : _It can easily be seen that the containment can be proper. This is illustrated in Figure 3._
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Rician Fading with Atmospheric and Rain Attenuation  
To model scattering effects beyond LOS, we incorporate a Rician fading model, where \(K\) represents the LOS-to-scattered power ratio. The Rician fading model under atmospheric and rain attenuation is given by  
\[P_{r}=P_{LOS}\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2}e^{-(A_{\text{atm}}+ \gamma_{r})\cdot d}\cdot(K+1)\cdot e^{-\sigma^{2}K} \tag{14}\]  
where \(P_{LOS}\) is the LOS power, \(\sigma^{2}\) is the variance of the Gaussian-distributed scattered component, and \(d\) is the path distance.
# IoT-Enabled Vehicle Speed Monitoring System
## 1 Introduction
The technology is critical for Pakistan's chaotic traffic where the conventional traffic monitoring system cannot be applied due to two main reasons: (1) continuous supply of power issues and (2) the highly irregular patterns in the traffic. These two issues restrict the existing methodologies to be applied in Pakistan and needs a robust system that can continuously check the speed of the vehicle irrespective of its trajectories even when the power is cut off for a period. From an engineering standpoint, the suggested method is more automated and efficient, with an accuracy of greater than 95%. The prototype's vision system is tough and reliable, and it can function in a variety of temperature conditions, ranging from rigid cold to scorching heat, or fine dust from the desert to freezing rain from northern regions. The organization of this paper is as follows: Section 2 presents a literature review to summarize existing research methodologies of speed monitoring systems. Section 3 provides a detailed description of the techniques applied in this project toward designing the prototype of SM&AD system for smart cities. This section further briefly explains the different pieces of equipment used in hardware design of proposed model, the various project modules and the optimal solution for the problem statement. The experimental results and the real-time functionality of project for testing the quality are given in Section 4. Finally, Section 5 concludes the paper, discusses some real-life use-cases and highlights the future works which can be carried out for further advancements in the field.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Temperature-Based Routing
#### 3.2.3 Trust and Thermal Aware Routing Protocol (TTRP)  
A trust and heat-aware routing protocol TTRP was proposed by [PERSON] et al. [23]. This paper considered two parameters--trust and temperature. Different from other routing methods, this routing method adds additional relay nodes, which only have the function of receiving and forwarding, do not participate in information collection, and are equipped with a higher energy to complete the function of forwarding information from other nodes.  
TTRP protocol includes three stages--trust estimation stage, routing discovery stage, and routing maintenance stage. The stage of trust estimation is mainly responsible for estimating the trustworthiness of the relay nodes (as shown in Figure 8), specifically through estimation of the buffer of \(\mathrm{R_{i}}\) and \(\mathrm{R_{k}}\) nodes, to determine whether the \(\mathrm{R_{j}}\) nodes are untrusted nodes. Routing phase is to find the best path without the hotpot and the trusted nodes. TTRP constructs a composite function to help the routing phase find a reliable path, quickly and accurately. The optimal path is the path with the smallest value of the composite function. The composite function is shown in (6):  
\[CF=\omega_{1}\times\mathit{Trust}+\omega_{2}\times\mathit{Temp} \tag{6}\]  
where \(\omega_{1}\) and \(\omega_{2}\) are the proportion weights of trust and temperature, respectively, and \(\omega_{1}+\omega_{2}=1\). The two weight factors can be changed, according to the actual situation, and the respective proportion of trust and temperature can be adjusted accordingly. TTRP uses two parameters of temperature and trust to find the best routing, which not only avoids the node with high temperature to become the current routing, but also distributes the network load and reduces the probability of nodes becoming hotpot, thus, ensuring the reliable transmission of node data. However, there may be a problem that ignores energy consumption and delay.  
Figure 7: HELLO message format of ER-ATTEMPT protocol.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 4 Results  
In this section, we demonstrate the performance of the Magill-based gating network and our optimal gating network in the presence of measurement biasing and dynamical model biasing through numerical simulation that is based on the InSight mission. Specifically, the simulation focuses on the last 45 days of the trajectory before Mars entry. The parameters that describe this simulation are defined by [PERSON] et al. [2]. We first summarize the filter bank performance when the system is properly modeled, then address how the filter bank performs with no biasing, when dealing with radiometric and optical measurement biasing, and finally introduce the gating networks to the problem of detecting dynamical model mis-modeling.
## 2 Materials and Methods
### Statistical Analysis  
Firstly, descriptive statistics through means and standard deviations were calculated for the 30 participants over the normalised continuum session considering each device: Garmin Strap, Coospo Strap, and Coospo Armband. Secondly, data normality was verified by the Shapiro-Wilk test. HR values from the seven effort and recovery stages were segmented, and intraclass correlation coefficients (ICCs) were calculated between devices (Coospo Strap-Garmin Strap, Garmin Strap-Coospo Armband, and Coospo Strap-Coospo Armband) as well as confidence intervals at 95% (95% CI) considering 1.00-0.81 (excellent), 0.80-0.61 (very good), 0.60-0.41 (good), 0.40-0.21 (reasonable), and 0.20-0.00 (deficient) [27]. Thirdly, an ANOVA with [PERSON]'s post-hoc analysis (\(p<0.05\)) between devices comparison was undertaken. This statistical method was chosen for its ability to account for within-subject variability, which enhances statistical power by minimising the effect of individual differences across repeated conditions. [PERSON]'s post-hoc test was applied following significant ANOVA results to determine specific pairwise differences between devices while controlling for the family-wise error rate, making it a compelling choice for multiple comparisons. Finally, Bland-Altman plots for between-device comparison was used to examine the level of agreement (LoA) between HR measurements [28]. The Bland-Altman plot was chosen because it represents the agreement between two measurement methods by plotting the difference against the mean of the two measurements. This approach allows for us to assess the mean bias between devices and the variability across the range of HR values,  
Figure 2: Study design: device placement and intensities performed by participants.  
highlighting any systematic biases or proportional differences. The limits of agreement (LoAs) indicate the range within which 95% of the differences between devices are expected to fall, offering a clear interpretation of whether the devices can be used interchangeably or if significant discrepancies exist in their measurements. Statistical analyses and plotting were performed using Python (Anaconda Navigator 2.6.2).
## 3 Results and Discussion
### Four-Port MIMO Array
This emphasizes the directional property of the MIMO antenna in both the E- and H-planes. Figure 7: Simulated S-parameters versus frequency of the proposed MIMO antenna. Figure 8: Variation in (**a**) ECC and (**b**) DG of 4-port MIMO antennas versus frequency. Figure 9: Radiation patterns of 4-port MIMO antenna: (**a**) E-plane, (**b**) H-plane at 28 GHz.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking
### Advantages of LTP over the Internet Protocol Suite  
In space communication channels, characterized by scheduled intermittence, LTP provides reliable data delivery, being widely adopted in systems and simulations. It is commonly researched for the scenario of interplanetary networks, being proposed in literature [43; 44; 45; 46], and even deployed in a space mission [47].  
The Internet protocol suite, TCP/IP, although successful for the terrestrial operating environments, suffers from strong performance degradation in space long-haul communications, in links with disruptions and high delays. One additional problem is due to the asymmetry of the channel rates, as TCP relies on acknowledgements which require an adequate data-rate in the reverse direction. In literature, comparisons of TCP and LTP have been presented and their performance evaluated in highly asymmetric channels [48; 49]. These are valuable studies for the application of high-layer protocols for FSO links, as it is common to have setups with limited uplink data-rates. The main differences between TCP/IP and BP/LTP are summarized below [36; 40; 48]:  
* The use of TCP requires the Internet Protocol (IP), while LTP is compatible with other lower layer protocols.
* In TCP, the **connection** is durable and unlimited in size, while LTP works with size restricted temporary **sessions** for transmission units (LTP blocks)--multiple sessions in parallel are allowed.
* The configuration of the connection in TCP is negotiable by a required three-way handshake, while LTP lacks the connection establishment.
* In TCP, the data is delivered in order within a connection. The nature of a session-oriented protocol, like LTP, manages the delivery of bytes in order within a session but does not guarantee the delivery of LTP blocks in order.
* LTP has a unidirectional data flow, using the reverse channel only for signaling.
* Unlike TCP, LTP does not have any mandatory mechanism for congestion control.
* LTP has a rate-based transmission speed that does not depend on acknowledgements, so losses do not reduce the speed, opposite to TCP which has a window-based congestion control.
* For the red parts of LTP, the acknowledgements--called report segments (RP)--are only sent when requested, or in checkpoints (CP), which are usually on the last segment of the block.
_Article_  
**Tracking Robot Location for Non-Destructive Evaluation of Double-Shell Tanks**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1,* 1**  
Washington State University Tri-Cities, School of Engineering and Applied Sciences, Richland, WA 99354, USA; [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (E.C.); [EMAIL_ADDRESS] (C.M.)  
Pacific Northwest National Laboratory, Richland, WA 99354, USA; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
Received: 30 August 2020; Accepted: 13 October 2020; Published: 19 October 2020  
**Featured Application: Robotic inspections of double-shell nuclear-waste storage tanks at the Department of Energy's Hanford site.**  
(1) Background: Non-destructive evaluation of double-shell nuclear-waste storage tanks at the U.S. Department of Energy's Hanford site requires a robot to navigate a network of air slots in the confined space between primary and secondary tanks. Situational awareness, data collection, and data interpretation require continuous tracking of the robot's location. (2) Methods: Robot location is continuously monitored using video image analysis for short distances and laser ranging for absolute location. (3) Results: The technique was demonstrated in our laboratory using a mockup of air slot and robot. (4) Conclusions: Location tracking and display provide decision support to inspectors and lay the groundwork for automated data collection.  
**Keywords: computer vision; image analysis; laser ranging**
## 3 Experiment and Analysis
### Experimental Setups
#### 3.1.3 Evaluation Criteria  
We evaluate our method using the following criteria: Precision, Recall, AP, and mAP. Precision is used to evaluate whether the predictions of objects are accurate, and Recall is adopted to evaluate whether all the objects have been inspected. They are computed as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
where TP, FP, and FN mean the number of positive samples with correct prediction, negative samples with false prediction and positive samples with incorrect prediction, respectively.  
AP represents the average precision, the value of which is the area under the Precision-Recall curve. [EMAIL_ADDRESS] is the value of AP when IoU threshold is set to 0.5, and [EMAIL_ADDRESS]:0.95 is the average value of AP when IoU threshold is set to 0.5 to 0.95 with an interval of 0.05. The mAP denotes the mean average precision, which is calculated by summing the APof all classes and then dividing by the number of classes. Formally, the AP and mAP are formulated as  
\[\begin{split}\text{AP}=\int\limits_{0}^{1}\text{Precision}(\text{ Recall})d(\text{Recall})\\ \text{mAP}=\frac{1}{\text{nc}}\sum\limits_{i=1}^{nc}\text{AP}_{i} \end{split} \tag{3}\]
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 4 Model (1): \(\left[K^{S}-n^{F}\right]\)-CNN Model
### Training and Testing of Model (1)  
The training process is monitored by evaluating both the training and validation losses over epochs, with the corresponding curves shown in Figure 5. Initially, both losses decrease rapidly, indicating that the model is learning effectively. As training continues, the training loss continues to decrease while the validation loss stabilizes, indicating some degree of overfitting. Despite tuning various hyperparameters, this issue could not be entirely eliminated. However, the validation loss remains stable at approximately \(10^{-3}\), which is within an acceptable range for permeability prediction as tested on unseen data. Since further training beyond 100 epochs does not improve generalization, but increases overfitting, we select _Early Stopping_ at this point. Thus, the model at 100 epochs is used for testing, ensuring a balance between accuracy and generalization.  
The scatter plots in Figure 6, which compare the predicted values to the ground truth values for the output variables \(n^{F}\) and \(K^{S}_{kk}\) (for \(k=1,2,3\)), further demonstrate the ability of the model to accurately predict the intrinsic permeability values and the corresponding porosity.  
Figure 5: Model (1): Training and validation loss function values over the number of weight updates (Epoch). The validation loss (blue curve) initially follows a similar trend to that of training loss (red curve) but starts to diverge after approximately 100 epochs, suggesting overfitting beyond this point.  
Figure 6: Model (1): CNN predictions vs. ground truth values of the intrinsic permeability components and porosity. The close alignment of the predicted values with the red line indicates a high prediction accuracy, confirming the model’s effectiveness in estimating permeability and porosity values.  
The model's performance can quantitatively be assessed using the \(R^{2}\) score, which is typically between 0 and 1 and calculated using the following formula:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-h_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-g)^{2}}\quad \text{with}\quad\left\{\begin{array}{l}y_{i}\quad:\text{True value for the $i$-th data point},\\ h_{i}\quad:\text{Predicted value for the $i$-th data point},\\ \bar{y}\quad:\text{Mean of the true values},\\ n\quad:\text{Number of data points}.\end{array}\right. \tag{29}\]  
The \(R^{2}\) score achieved in this model is \(\approx\)0.985, demonstrating a high level of accuracy in predicting the output variables based on unseen data.
## 2 Preliminaries
### Network Coding  
Traditionally, relay nodes typically forward information directly from source nodes to destination nodes, without any processing. However, with network coding, relay nodes encode messages and send the encoded information to the destination. This enables the network to achieve its maximum theoretical transmission capacity, as stated by the max-flow min-cut theorem.  
The PNC concept, first presented in [10], builds upon the idea of network coding but applies it to the physical layer. Unlike traditional relay nodes, PNC-enabled relays do not decode signals individually; they instead process the combined signals directly  
Figure 1: The relay-assisted VLC systems model.  
through mapping and modulation, which achieves network coding. This approach leads to a decrease in transmission time slots and an increase in throughput rates.
## 3 Problem Statement  
Previous studies have shown that higher accuracy and better performance can be achieved by using detailed models to forecast PV power output in different weather conditions. In these works, NWP model output is an important data source for power forecasting and weather status pattern recognition. Most PV power plants in China are located in relatively remote locations, which are usually lacking in high-resolution local NWP data. Hence, to realize the classified power forecasting in different weather conditions for these PV plants, a weather classification method based on historical data of the PV plant is often required.  
However, sufficient and comprehensive historical data relies on a long-term historical measured weather and power dataset, which is not generally available for a newly built PV plant. On the other hand, the weather condition at an individual PV plant is determined by regional information and seasonal factors, which means, for a given PV power plant, the data collected in some weather  
Figure 4: The mapping of feature vectors from a low dimension (shown as triangles) to a higher dimension (shown as squares).  
conditions may be plentiful while data in other weather conditions may be scarce or non-existent. This kind of imbalance in the distribution of data in different weather conditions will change as more operational data is collected.  
The accuracy of classification results depends both on the data status and the applied classification model and method, as shown in Figure 5. From the perspective of data status, the factors influencing accuracy can be summarized in three aspects: the sample scale of training data, the total number of categories, and the distribution of the data in different categories. From the perspective of a classification method, the factors most influencing accuracy are the classifier and the parameters of the applied classifier.  
Therefore, in the face of the different data status of an individual PV plant during the data accumulation process, multiple classification methods with optimized parameters are desperately needed. The purpose of this study is to assess the performance of SVM and KNN methods with varying data status through simulations. By analyzing and evaluating the simulation results, the mechanism of how the two factors influence classification performance can be acknowledged.