esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 OTFS Modulation
### Transmitted Signal Model  
In OTFS systems [5; 7], the TX arranges \(MN\) symbols in the delay-Doppler grid, referred to as \(I_{DD}\), where \(M\) is the number of subcarriers and \(N\) is the number of time slots of the OTFS frame. The DD-domain symbols are then converted to the time-frequency domain through inverse symplectic finite Fourier transform (ISFFT). Denoting with \(\mathbf{X}\in\mathbb{C}^{M\times N}\) the delay-Doppler symbols multiplexed in the DD domain, the corresponding symbols in the time-frequency grid are obtained as  
\[X_{tf}[l,k]=\frac{1}{\sqrt{MN}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}X[m,n]e^{j2\pi \left(\frac{nk}{N}-\frac{ml}{M}\right)}. \tag{1}\]  
After that, the time-domain OTFS signal is obtained through the the Heisenberg transform as  
\[s(t)=\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}X_{tf}[l,k]g_{tx}(t-kT)e^{j2\pi l\Delta f (t-kT)}, \tag{2}\]where \(g_{tx}(t)\) is the transmit-shaping pulse of duration \(T\). The continuous-time signal in Equation (2) has a total duration of \(NT\) and a bandwidth of \(M\Delta f\), where \(T\) corresponds to the time slot duration and \(\Delta f=1/T\) is the subcarrier spacing. Thus, the delay and Doppler resolutions are, respectively,  
\[\tau_{\text{res}}=\frac{1}{M\Delta f},\;v_{\text{res}}=\frac{1}{NT}. \tag{3}\]
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments
### Evaluation Metrics  
To quantitatively assess the registration performance, we follow the evaluation protocol as in the Lung250M-4B benchmark [16], adopting the Target Registration Error (TRE) as our primary metric. In addition to the mean TRE, we report the \(\{25,50,75\}\) percentile TRE values to provide a more comprehensive view of the registration accuracy across different anatomical regions and difficulty levels. Moreover, we evaluate the alignment quality using the Dice Similarity Coefficient (DSC) between the transformed source and the target mask. A higher DSC indicates a better anatomical overlap, while the TRE assesses the quality of landmark registration.
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 3 Improved Information Maximizing Generative Adversarial Nets Extreme Scene Generation
### Power System Operation Simulation Model  
To verify the effectiveness of the InfoGAN-generated scenario proposed in this paper, the following power system model is established. A power system to preserve power supply and consumption is taken as the objective function, and the level of the power system to preserve power supply and consumption under the scenario is evaluated.  
\[\text{min}\ F=F_{L}+F_{\text{Re}} \tag{8}\]  
where \(F_{L}\) is the cost of shedding load; \(F_{W}\) is the curtailment penalty of renewable energy. The specific calculation formulas are as follows:  
\[F_{L}=\lambda^{load}\sum_{t=1}^{T}\sum_{g=1}^{N_{\text{C}}}\Delta P_{t,j,s}^{D} \tag{9}\]  
\[F_{W}^{\text{S}}=\lambda^{RE}\sum_{t=1}^{T}\sum_{i=1}^{N_{BE}}\Delta P_{t,j,s}^ {\text{Re}} \tag{10}\]  
Power system constraints are below:  
\[\sum_{i=1}^{N_{f}}P_{t,i,s}^{\text{G}}+\sum_{i=1}^{N_{\text{Reg}}}\Big{(}P_{t, i,s}^{\text{Re}}-\Delta P_{t,j,s}^{\text{Re}}\Big{)}-\sum B_{n,m}\big{(}\delta_{t,n,s}- \delta_{t,m,s}\big{)}=\sum_{j=1}^{N_{D}}\Big{(}P_{t,j,s}^{D}-\Delta P_{t,j,s}^ {D}\Big{)} \tag{11}\]  
\[P_{t,i,s}^{\text{W},f}-P_{t,i,s}^{\text{W}}\geq 0 \tag{12}\]  
\[-P_{n,m}^{L,\text{max}}\leq B_{n,m}(\delta_{t,n,s}-\delta_{t,m,s})\leq P_{n,m} ^{L,\text{max}} \tag{13}\]  
\[-\pi\leq\delta_{t,n,s}\leq\pi \tag{14}\]  
\[\delta_{t,1,s}=0 \tag{15}\]  
\[u_{t,j,s}^{\text{G}}P_{t,\text{min}}^{\text{G}}\leq P_{t,j,s}^{\text{G}}\leq u _{t,j,s}^{\text{G}}P_{t,\text{max}}^{\text{G}} \tag{16}\]  
\[P_{t,i,s}^{\text{G}}-P_{t-1,i,s}^{\text{G}}\leq\Delta P_{t,i,j,\text{max}}^{ \text{G}} \tag{17}\]  
\[P_{t-1,i,s}^{\text{G}}-P_{t,i,s}^{\text{G}}\leq\Delta P_{t,j,\text{max}}^{ \text{G}} \tag{18}\]  
Figure 3: Control parameters training framework.  
\[\left\{\begin{array}{l}U^{on}_{t,i,s}\geq\left(u^{G}_{t,i,s}-u^{G}_{t-1,i,s} \right)\\ U^{on}_{t,i,s}\geq 0\end{array}\right. \tag{19}\]  
\[\left\{\begin{array}{l}U^{off}_{t,i,s}\geq\left(u^{G}_{t-1,i,s}-u^{G}_{t,i,s} \right)\\ U^{off}_{t,i,s}\geq 0\end{array}\right. \tag{20}\]  
\[\left\{\begin{array}{l}\left(T^{on}_{t-1,i,s}-T^{on}_{i,\min}\right)\left(u^ {G}_{t-1,i,s}-u^{G}_{t,i,s}\right)\geq 0\\ \left(T^{off}_{t-1,i,s}-T^{off}_{i,\min}\right)\left(u^{G}_{t,i,s}-u^{G}_{t-1, i,s}\right)\geq 0\end{array}\right. \tag{21}\]  
Constraint (11) represents the energy balance of the power system. The wind power consumption constraint is depicted in Constraint (12). Constraint (13) imposes restrictions on the transmission line capacity. Constraints (14) and (15) constrain the voltage angle. The power outputs are denoted by Constraint (16), and the ramping up/down of units is governed by Constraints (17)-(19). Constraints (19) and (20) limit the on/off status of the units, while the on/off time constraint is expressed in Constraint (21).
## 5 Secure End-to-End Encryption for Iot Communication Scheme
### Communication  
This phase is an important part of the protocol in the communication process between devices in the MQTT network. In this phase, all IoT devices that have the same group key (\(K_{G}\)) can communicate and exchange information securely. The following is the proposed communication protocol:  
Figure 3: Group Key Generation Phase.  
Set-up environment:  
Device A and Device B have Key Group \(K_{G}\)  
Device A generates a Nonce to generate a session key \(K_{C}\) as communication key  
Encrypted Channel Creation Process  
Calculates the session key ( \(K_{C}\)) used for communication. \(K_{C}=K_{G}\oplus\)_Nonce_  
Calculating the S signature to check the validity of the communication key \(K_{C}\): \(S=hash\) (\(Nonce||hash(KG)^{128}\))  
Device A sends an encrypted message C to B via MQTT Broker: \(E_{KC}\{M,\ t_{A}\},S\), \(Nonce\)  
Device B receives encrypted messages from Device A via the MQTT broker. Device B does calculate the following:  
Calculating the Signature \(S^{\prime}\) with the nonce information sent and \(K_{G}\) held: \(S^{\prime}=hash\) (\(Nonce||hash(KG)^{128}\))  
eq S\), the message is not processed. However, if \(S^{\prime}\
eq S\), then go to step c.  
Performs session key calculations for communication with received nonce information. \(K_{C}=K_{G}\oplus\)_Nonce_  
Decrypt the message with \(K_{C}\) calculated to get the message. \(D_{KC}\{E_{KC}\{M,\ t_{A}\}\}\)  
Device B gets messages M and \(t_{A}\), the information \(t_{A}\) is used to check the freshness of the received message.  
The protocol in the communication phase above can be visualized with a communication scheme as shown in Figure 4.
## 1 Introduction
### Action Selection Policy  
Each possible pair of state-action must be evaluated for each state that the agent has visited in order to ensure that the estimated/learned Q-value function is the optimal one (i.e., corresponds to the most suitable action policy). In this work, the Boltzmann exploration policy has been used [28]. Each possible action has a probability of selection by the agent, which is calculated as follows:  
\[p(S_{t},a_{i})=\frac{e^{\frac{Q(S_{t},a_{i})}{\tau}}}{\sum\limits_{1}^{|\mathbf{ A}|}e^{\frac{Q(S_{t},a_{i})}{\tau}}} \tag{3}\]  
where \(\tau\) is the Boltzmann exploration \"temperature\", \(a_{i}\) is the \(i\)-th possible action, \(|\mathbf{A}|\) (size of the action-space) is the total number of alternative actions in each state and \(Q(S_{t},a_{i})\) is the Q-value for the \(i\)-th action in state \(S_{t}\).  
The temperature function is given by:  
\[\tau=\left\{\begin{array}{cl}\tau_{\min}+(1-\frac{N}{N_{\max}})\cdot(\tau_{ \max}-\tau_{\min})&\text{ if }N\leq N_{\max}\\ \tau_{\min}&\text{ if }N>N_{\max}\end{array}\right. \tag{4}\]  
where \(\tau_{\min}\), \(\tau_{\max}\) are the minimum and maximum, respectively, \"temperature\" values, \(N\) is the current number of visits of the agent in the specific state and \(N_{\max}\) is the maximum number of visits of the agent.  
The value of \(\tau\) is positive and controls the randomness of the action selection. If the value of \(\tau\) is high, then all values of the probabilities of each action, which are calculated using (3), are similar. This results in a random action selection depending on the parameter \(N_{r}\), which is a random number in the range (0, 1). Section 2.6 presents the way that \(N_{r}\) affects the action-selection policy. As the number of visits, \(N\), to a specific state increases, the value of \(\tau\) reduces. After a certain number of visits, \(N_{\max}\), the value of \(\tau\) becomes equal to its minimum value. This means that the exploration process has been accomplished in that state and the agent chooses the action with the highest Q-value in that state.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### Software Updates  
Updates to the software of HYPSO-1 can be of two types: application software updates and firmware updates. Application software updates are newer versions of the main application or the pipeline application file stored on the SD Card or eMMC. Firmware updates are updates of the primary boot image on the SD Card and newer versions of the FPGA image file or additional FPGA modules. At the launch of HYPSO-1, the golden image and the primary image were identical. During nominal operations, the primary image is expected to be updated while the golden image is to be kept at its initial launch version. The motivation behind distinguishing application software and firmware updates  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Motivation** \\ \hline SnK & Improve data quality \\ DG & Pass target information to in situ agents \\ SOM & Package data for quick download \\ SVM & On-board data segmentation \\ Subs & If only parts of a cube are interesting \\ RGB & Preview a capture \\ \hline \hline \end{tabular}
\end{table}
Table 4: Smile and Keystone (SnK) correction: camera calibration. Direct Georeferencing (DG): on-board estimation of coordinates of the spatial pixel.Self-Organizing Map (SOM): dimensionality reduction. Support Vector Machine (SVM): segmentation. Subsampling (Subs.): extract a subset of spatial pixels. Red Green Blue (RGB): Composition of an image using three bands from the datacube.  
Figure 7: Architecture of the on-board processing pipeline. The six modules are the modules currently implemented and tested on the ground. See Table 4 for a description of the stages. In this example, indicated by the dashed arrows, the stages SVM, RGB and DG and SnK are picked to be run via the pipeline configuration file.  
is primarily due to their size and the level of risk associated with performing the updates. The size of a boot image is ca. 17 MB and the size of an application file is 0.5-1 MB. Updates will likely increase these sizes, thereby increasing operational complexity as the upload rate is limited. A newer version of the main application identified by a file path and stored on the SD Card can be run on command. Similarly, the pipeline application is run by command and the version is identified by path. Thus, multiple versions of both the main application and pipeline application can be stored on-board and run as required. See Table 5 for an overview of files to be stored on the permanent storage media on the OPU. Experimental newer versions, that may contain bugs, are run as required. A safe version of the main application that is stored in the hash-verified boot image is run after power-on and boot. This enables error recovery via reboot when running newer versions.
## 5 Simulation Results and Discussions
### Simulation Results and Analysis
#### 5.2.3 t-SNE Feature Visualizations  
In order to more intuitively observe the effect of the DC-CAAE on the data dimensionality reduction, we visualized the output potential representation \(z\) and compressed it to a two-dimensional plane for observation. Figure 7 shows the clustering space of different networks, each represented by a different color. All methods reduced the data dimension to 100 dimensions. It can be seen that the distance between each class of features extracted by the ordinary CAE is not obvious, and some classes are mixed together. The DC-CAAE method achieves a more friendly clustering space than the original coded image space and the space before the joint training.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **NMI** & **AC** &
\begin{tabular}{c} **Silhouette** \\ **Coefficient** \\ \end{tabular} & **ARI** & **V-Measure** \\ \hline Minibatch k-means & 0.894 & 0.905 & 0.418 & 0.829 & 0.901 \\ clustering & & & & & \\ \hline Spectral clustering & 0.908 & 0.908 & 0.444 & 0.826 & 0.915 \\ \hline BIRCH & 0.903 & 0.906 & 0.427 & 0.819 & 0.910 \\ \hline GMM & 0.904 & 0.908 & 0.428 & 0.822 & 0.911 \\ \hline
**k-Means clustering** & **0.911** & **0.922** & **0.422** & **0.844** & **0.917** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of DC-CAAE with different clustering methods.  
Figure 6: Convergence trend of overall cluster loss during training.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
###### Abstract
Determining the trajectory is, therefore, a complex process that is largely dependent on the quality of the software that comes with the scanner. Establishing the achievable accuracy of the resulting data in advance is, therefore, more complicated than in the case of static scanners. Besides the difficult determination of the accuracy in SLAM scanners, their results can be also expected to yield higher noise than static scanners [20]; the use of suitable processing methods can, however, greatly improve the final result [21]. SLAM scanners are nowadays used for many purposes previously reserved for static scanners; this is especially true in building interiors, where this technology offers great promise [22]. Importantly, new SLAM algorithms and systems, including hardware [23] are still being developed and the whole SLAM technology is moving forward rapidly. A generational change is currently underway, with more accurate SLAM sensors coming to the market. According to their manufacturers' declarations of accuracies and reliabilities, such scanners should be already suitable for surveyor work, being (at least in some parameters) on par with the terrestrial static systems. These improvements can be seen, for example, in the increased number of channels registered by the scanning head from 16 to 32 or in the improved accuracy of distance determination. Although the evaluation of the suitability and accuracy of SLAM scanners has been discussed in several studies [24; 25; 26], detailed testing and comparison of multiple scanners of older and newer generations under real interior conditions is, however, not available so far. This study aims to evaluate (in particular) the accuracy of multiple SLAM scanning systems available on the market in a representative building interior (a technical floor of an administrative building) and to compare the results of the newer and older SLAM scanners using reference data obtained with a high-accuracy static scanner.
# Chaotic Evolutionary Programming for an Engineering Optimization Problem
## 2 Economic Load Dispatch Problem  
The economic load dispatch problem aims to minimize the power generation's cost while satisfying the constraints of expected load demand and the generator's operation. In the case of the multi-fuel load dispatch problem, the power generators have the option of multiple fuels, and each unit represents several piece-wise, quadratic functions reflecting the effect of fuel change. The multiple fuel options and valve point loading effect result in the multimodal and discontinuous nature of the problem. A multi-fuel ED problem is mathematically expressed as follows:  
Minimize the operating cost:  
\[F(P)=\sum_{j=1}^{N_{g}}(a_{jm}P_{j}^{2}+b_{jm}P_{j}+c_{jm}+|d_{jm}\sin e_{jm}( P_{jm}^{min}-P_{j})|)\quad(P_{jm}^{min}\leq P_{j}\leq P_{jm}^{max}) \tag{1}\]  
where \(P_{j}\) is the generated real power and \(P\)= [\(P_{1}\), \(P_{2}\),..., \(P_{Ng}\)]\({}^{T}\). \(N_{g}\) is the number of generators. \(a_{jm}\), \(b_{jm}\), \(c_{jm}\), \(d_{jm}\), and \(e_{jm}\) are the thermal generators' cost coefficients of the \(j\)thgenerator's \(mt^{th}\) fuel option. \(P_{jm}^{min}\) and \(P_{jm}^{max}\) are the generator's lower and upper limits for the \(mt^{th}\) fuel option.  
The cost objective function is subject to:  
1. The power balance equality constraint: \[\sum_{j=1}^{N_{g}}P_{j}-(P_{D}+P_{L})=0\] (2)
2. The generator operating limits: \[p_{j}^{\min}\leq P_{j}\leq P_{j}^{\max}\qquad(j=1,2,\ldots,N_{g})\] (3)
3. The ramp rate limit. * As generation increases: \[P_{j}-P_{j}^{0}\leq UR_{j}\qquad\big{(}j=1,2,\ldots,N_{g}\big{)}\] (4)
4. As generation decreases: \[P_{j}^{0}-P_{j}\leq DR_{j}\qquad\big{(}j=1,2,\ldots,N_{g}\big{)}\] (5)
5. Prohibited operating zone constraint: \[P_{j}^{\min}\leq P_{j}\leq P_{j,1}^{L}\quad\big{(}j=1,2,\ldots,N_{g} \big{)}\] (6) \[P_{j,i-1}^{U}\leq P_{j}\leq P_{j,i}^{L}\quad\big{(}i=1,2,\ldots, N_{zj};j=1,2,\ldots,N_{g}\big{)}\] \[P_{N,Nzj}^{U}\leq P_{j}\leq P_{j}^{\max}\quad\big{(}j=1,2,\ldots, N_{g}\big{)}\]where \(P_{D}\) and \(P_{L}\) are the forecasted demand and transmission loss of the network, respectively. \(N_{zj}\) the is number of prohibited zones of the \(j\)th generator. \(P_{j}^{0}\) is the previously generated power. \(UR_{j}\) and \(DR_{j}\) are the up-ramp limit and down-ramp limit of the \(j\)th generator. \(P_{j,i}^{L}\)_and_\(P_{j,i}^{U}\) are the lower and upper range of the \(j\)th prohibited zone respectively of the \(j\)th generator
## 3 Results and Discussion
### Case 2: Experimental Analysis of QoS Guarantee
#### 3.3.2 QoS Variety  
When the QoS expectations are varied, Figure 7 shows that the performance gaps among comparisons will decrease by increasing the number of users. In UDN modeling, the room size, as shown in Table 3, is 36 m\({}^{2}\). For high user density, when the number of users is greater than 9, NOMA FL can reach the AUDR of FTPA. This means that NOMA FL is applicable to the proposed VLC UDN scenario. As shown in Figure 8, with the normalization of capacity, our proposed method outperformed PLS and reached the performance level of FTPA under UDN conditions. In Figures 6-8, NOMA FL and OFDMA FL based on dynamic user priority are shown to be adaptive to changes in QoS requirements which are different from the fluctuations in PLS and FTPA based on static user priority.  
Figure 6: Comparisons of user satisfaction with the QoS expectation at 60 Mbps.  
Figure 7: Comparisons of AUDR with varied QoS expectations and number of users.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Evaluation Metrics  
In object detection experiments, selecting appropriate evaluation metrics is crucial. This paper uses precision (P), recall (R), mean average precision (mAP), and frames per second (FPS) as the primary metrics. Precision and recall assess detection accuracy and completeness, mAP evaluates overall model performance across categories, and FPS indicates real-time efficiency.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 3 Methodology
### ILP Formulation for NBTI Mitigation and Leakage Reduction Only  
ILP is a kind of mathematical optimization approach consisting of an objective function and a set of linear constraints in a specific format, as follows [29; 30]:  
\[\begin{array}{l}\min\mathbf{C}^{T}\mathbf{X}\\ s.t.\ \mathbf{UX}\leq\mathbf{b}\ \text{and}\ \mathbf{EX}=\mathbf{d}\end{array}. \tag{10}\]  
In Equation (10), \(\mathbf{X}\) represents the optimization variables, which is the binary state (0/1) of the circuit node, \(\mathbf{C}\), \(\mathbf{d}\), and \(\mathbf{b}\) are vectors of coefficients, and \(\mathbf{U}\) and \(\mathbf{E}\) are matrixes of coefficients. The ILP solvers can be applied to find the optimal value for Equation (10). At present, most of the commercial ILP solvers, such as CPLEX and LINGO, use a branch-and-bound (B&B) algorithm to solve binary integer programming problems [31]. The B&B algorithm searches for an optimal solution by solving a series of linear programming (LP)-relaxation problems, in which the binary integer requirement on the variables is replaced by the weaker constraint \(0\leq x\leq 1\). Then, the algorithm implements the following steps: Search for a binary integer feasible solution; Update the best binary integer feasible point found so far as the search tree grows; Verify that no better integer feasible solution is possible by solving a series of linear programming problems. Compared to the random Monte Carlo simulation and heuristic method, an ILP formulation can find the global best solution. However, the ILP formulation is proved to be an NP-complete problem and could potentially search all \(2^{n}\) binary integer vectors, where n is the number of variables; some constrains, such as the maximum run-time and number of iteration, should be applied. In this paper, the complexity of the ILP formulation for NBTI mitigation and leakage reduction are not high; therefore, all the problems can be solved in a reasonably short time.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Packet Collision Sensing
#### 3.2.3 Feedback Provider Qualification Based on In-Band Emission
On the other hand, if the received packet can be decoded (line 10), the piggybacked feedback information (location of the collision suspected by the transmitter of the packet) is compared with the collision event entries in the feedback queue that the receiver has sensed so far (line 13). If there is a match, the entry is knocked out of the queue because it has just been feedbacked by another vehicle (line 14). ```
1:procedureFPQ(\(n\), Nsubch, \(\alpha\), \(Q\))
2:\(n\): current slot
3:Nsubch: no. of subchannels
4:\(\alpha\): IBE weight factor
5:Q: feedback queue
6:
7:for\(k=1\); \(k<\)Nsubch; \(k=k+1\)do
8:\(\gamma^{\prime}(R_{k}^{n})\leftarrow\gamma(R_{k}^{n})-\sum\limits_{i\
eq k} \alpha_{i,k}\gamma^{\prime}(R_{i}^{n})\)
9:
10:if\(\sigma(R_{k}^{n})\geq\theta_{\sigma}\)then
11://condition(1): decoded \(-\) no feedback
12:\(\varphi=\text{extract}-\text{feedback}(R_{k}^{n})\)
13:if\(\varphi\in Q\)then
14:\(Q\gets Q\backslash\varphi\)//knock\(\varphi\) out
15:else if\(\gamma^{\prime}(R_{k}^{n})\leq\theta_{\gamma}\)then
16://condition (4): outside ROI \(-\) no feedback
17:else if\(\frac{\gamma^{\prime}(R_{k}^{n})}{\sum\limits_{i\
eq k}\alpha_{i,k}\gamma^{ \prime}(R_{i}^{n})}\leq\theta_{\sigma}\)then
18://condition (5): IBE suspected \(-\) no feedback
19:else
20:enqueue(-\(k\),\(n\), \(Q\)) // add the location to feedback queue
```  
**Algorithm 1**Feedback provider qualifier (FPQ)