esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 3 Results and Discussion
### Calculation Conditions
#### 3.1.3 Space-Based AVD System  
At present, the space-based infrared detection system is represented by OPIR and SBIRS of the United States; however, there is no space-borne infrared system specialized in AVD. According to the \(GSD\) need for a space-based AVD system [12] and a published article [21], the typical parameters of AVD system were estimated, which are listed in Table 1. The superiority of the proposed method is illustrated by comparing it with the traditional fixed-parameter AVD system.  
The spectral band dynamically adjusted from 2.0 to 4.5 \(\upmu\)m, and the integration time and the capacitance gear were adjustable. The performance of the proposed method over different detection scenarios was compared with a fixed-parameter AVD system with a fixed-parameter set (Detection band: 2.7-3.0 \(\upmu\)m, Integration time: 100 ms at daytime, 200 ms at nighttime, Default integration capacitance: 3 \(\times\) 10\({}^{6}\) e\({}^{-}\)).  
The proposed method and other method were implemented under MATLAB R2019b with an Intel Core 2.80 GHz processor and 8 GB of physical memory. \(JSNCR\) results were analyzed after executing the parameter adjustment compared with the fixed-parameter system, and several conclusions and suggestions were achieved.
# Anticipatory Troubleshooting
## 3 Troubleshooting with Survival Analysis
### Survival Functions  
Survival functions give the probability that a device, a system component, or other object of interest will survive beyond a specified time. Survival functions can be obtained by analysis of the physics of the corresponding system [1] or learned from past data [2].  
**Definition 2** (Survival Function).: _Let \(T_{C}\) be a random variable representing the age at which \(C\) will fail. A survival function for \(C\), denoted \(S_{C}(t)\), is the cumulative distribution function of \(T_{C}\), that is: \(S_{C}(t)=Pr(T_{C}\geq t)\)._  
Figure 1 illustrates three possible survival curves generated by a Weibull decay function \(e^{-(\lambda\cdot t)^{b}}\) where \(t\) is the age (the \(x\)-axis). The \(y\)-axis represents the probability that a component will survive (i.e., not fail) \(t\) time units (e.g., months). \(\lambda\) and \(b\) are parameters. \(\lambda\) is known as the rate parameter in a Poisson process, and is related to the expected number of events occurring in a bounded region of time. In our case it represents the failure rate. The \(b\) parameter is known as the hazard rate. This parameter depicts the instantaneous rate in which events occur. The three curves plotted in Figure 1 correspond to the case that  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Action** & **System State (\(\mathcal{G}\))** & **Agent’s Belief \(B\)** \\ \hline Start & \(\{\
eg h(C_{1}),h(C_{2}),h(C_{3})\}\) & \(\{h(C_{3})\}\) \\ \(Sense_{C_{2}}\) & \(\{\
eg h(C_{1}),h(C_{2}),h(C_{3})\}\) & \(\{h(C_{3}),h(C_{2})\}\) \\ \(Sense_{C_{1}}\) & \(\{\
eg h(C_{1}),h(C_{2}),h(C_{3})\}\) & \(\{h(C_{3}),h(C_{2}),\
eg h(C_{1})\}\) \\ \(Repair_{C_{1}}\) & \(\{h(C_{1}),h(C_{2}),h(C_{3})\}\) & \(\{h(C_{3}),h(C_{2}),h(C_{1})\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of a system state and an agent’s beliefs affected by the troubleshooting actions.  
\(b=1\) and three values of the \(\lambda\) parameter. Note that small \(\lambda\) is associated with a higher probability to survive longer, whereas large \(\lambda\) results in a lower probability to survive for a long time. An increasing hazard rate is appropriate since we expect younger components to be more likely to survive, i.e., to have the derivative of the survival function decrease as the component's age increases.  
The challenge is how to compute the probability that a component \(C\) caused a system failure given its age and survival function. In most systems, faulty components fail intermittently; i.e., a component may be faulty but acts normally. Thus, the faulty component that caused the system to fail may have been faulty even before time \(t\). Therefore, we estimate the probability of a component \(C\) of age \(C_{age}\) to have caused the system failure by the probability that it has failed any time before the current time. This probability is directly given by \(1-S_{C}(C_{age})\).
## 2 Principles
### Light Plane Calibration Based on a Single Circle Target
#### 2.2.3 Progressive Solution of the Light Plane Equation  
As shown in Figure 5, the light plane intersects the circle target to form a laser stripe at the \(i\)th position of the target. Meanwhile, the image plane captures the laser stripe to form the line segment \(L_{i}\). In Figure 2, plane \(\Pi_{1}\) can be obtained through the camera model, while plane \(\Pi_{2}\) of the circular target is determined as specified in Section 2.2.2. However, two sets of possible parameters of plane \(\Pi_{2}\) corresponding to light stripes \(Ls_{A}\) and \(Ls_{B}\) are obtained in Section 2.2.2, which causes ambiguity. Generally, \(Ls_{A}\) and \(Ls_{B}\) satisfy two requirements: (1) they can be noncoplanar lines and (2) one of them should be on the light plane. Therefore, the following procedure has been proposed:Initial light plane determination  
_Step 1_: Two positions of the target are introduced, which correspond to four possible light stripes (\(Ls_{A,1}\), \(Ls_{B,1}\), \(Ls_{A,2}\), and \(Ls_{B,2}\), where \(Ls_{A,1}\) and \(Ls_{B,1}\) denote the first target position and \(Ls_{A,2}\) and \(Ls_{B,2}\) represent the second target position).  
_Step 2_: Four straight lines are combined to form \(N\) (\(N\leq 4\)) planes \(\Pi_{LS,i}\) (\(i=1,2,\ldots,\)\(N\)). The condition \(N\leq 4\) is applied because a plane does not exist if the two lines are non-coplanar.  
_Step 3_: A new position of the target is introduced, and the two corresponding possible light stripes are judged against the planes formed in step 2. If the two new light stripes are not on the current plane, the latter is not a light plane.  
_Step 4_: If there is only one plane is left after step 3, it is the light plane. Otherwise, step 3 is repeated until only one plane is left.  
Progressive refining of the light plane  
After one initial light plane is determined, its parameters must be further optimized. A new position of the target is introduced due to the existence of two possible light stripes (e.g., \(Ls_{A}\) and \(Ls_{B}\)). If one of these stripes (\(Ls_{A}\)) is located on the light plane and the other stripe (\(Ls_{B}\)) is located away from the light plane, the light stripe \(Ls_{A}\) will be added to fit (refine) the light plane. Finally, the iteration termination conditions are formulated as follows. (1) When the new position of the target is introduced, the variation in the light plane parameters is less than 0.1%. (2) The calibration images are exhausted.
## 3 Results and Discussion
### Geographical Patterns of Trend Types
#### 3.2.3 Trend Reversals  
As mentioned before, there is a well documented agreement on a net greening pattern since the early 1980s, especially in the Northern Hemisphere [2, 13]. It has, however, also been suggested (and debated) that vegetation activity peaked around the millennium transition, after which browning trends took over, most notably in the Southern Hemisphere [8, 9]. For the majority of the trend reversals detected in this study, greening turned into browning (Figure 3(b), inset), which substantiates the hypothesized reduction in vegetation activity. The timing of these reversals, for the better part, coincides with the late 1990s and early 2000s (Figure 5). Substantially fewer regions with reversals into greening were found and most of these were confined to the latest years of the GIMMS time span. Therefore, they may represent a short-lived process. In terms of number of grid cells, the area under browning trends increased from _ca._\(20\times 10^{4}\) to _ca._\(28\times 10^{4}\), which is equivalent to an increase of 40%.  
The greening-to-browning reversals appeared geographically diverse, but prominent regions include parts of Western Australia, the Sahel, Argentina, northern Kazakhstan and the Middle East. In these regions, a monotonicity-assumption may cause reversed trend segments to average out, resulting in a stable qualification or a relatively low net change. Indications for this effect were, for instance, found in Australia. Greening was previously found in Western Australia and browning in the central parts of Australia, while the eastern regions appeared stable (Figure 4). According to Figure 3(b), however, large parts of the central and eastern regions have experienced a recent green-up reversal while a greening-to-browning hotspot was found in Western Australia. The green-up can likely be attributed to the recent rainfall in eastern Australia [48], which boosted vegetation activity. The western browning reversal occurred earlier, _i.e._, around the millennium transition, and may be attributable to droughs [9, 48, 49]. Similar reversals were found in a latitudinal band in central Asia, with a longitudinal gradient in the timing (late 1980s in the West up to late 1990s in the East).  
In the Sahel, similar reversals were found before the time span of satellite records, coinciding with the onset of a long-term drought around 1969 [50]. An alternation between two land cover states, a \"wet Sahel\" and a \"dry Sahel\", each of which may persist for decades at a time, has been suggested by [PERSON] _et al._[50]. Shifts between the two states are preceded and induced by gradual processes like land degradation and sea-surface temperature. In large parts of Africa, oceanic oscillations are likely to play a role in these vegetation dynamics [51].
## 4 Conclusions  
In conclusion, the rectangular MGSM array beams composed by \(N\times D\) MGSM beams in rectangular symmetry are given, and the intensity and effective beam size of MGSM array beams in free space and in non-Kolmogorov turbulent atmosphere were investigated. Based on the derived equations, the results show that the MGSM array beams propagating through non-Kolmogorov turbulent atmosphere will become a Gaussian beam caused by the non-Kolmogorov turbulent atmosphere, while array beams propagating through free space will translate into flat-topped beams. With an increase in \(C_{n}^{2}\) and \(L_{0}\), or and decrease of \(\alpha\) and \(l_{0}\), the Gaussian-like beam will have a larger beam size. With M increases or \(\sigma\) decreases, the array beams will also evolve into beams with a larger spot. The obtained results may be beneficial for free space optical communication.  
Figure 8: The \(W_{x}\) of \(N\times D\) MGSM array beams propagating through non-Kolmogorov turbulent atmosphere. (**a**) different \(\alpha\), (**b**) different \(L_{0}\), and (**c**) different \(l_{0}\).  
Figure 7: The \(W_{x}\) of \(N\times D\) MGSM array beams propagating through non-Kolmogorov turbulent atmosphere. (**a**) different \(M\), (**b**) different \(N=D\).  
**Author Contributions:** [PERSON]. (Data curation, Writing--original draft preparation); [PERSON]. (software, Validation); [PERSON]. (formal analysis); [PERSON]. (software); [PERSON]. (Supervision, Writing--Review, Editing, and Validation); [PERSON]. (Supervision, Writing--Review, Editing, and Validation). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by National Natural Science Foundation of China [11604038, 11404048, 11875096], and the Fundamental Research Funds for the Central Universities [3132019182, 3132019184].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments Results and Discussion
### Anchor Boxes Clustering  
Generally, in the YOLO-v3 model, the k-means clustering algorithm is used to cluster analysis on the labeled boxes of the COCO dataset. However, the k-means clustering method has large randomness in the selection of initial clustering points. In this paper, the k-means++ clustering algorithm was adopted to cluster analysis on the dataset of 'InSF-detection', and the relationship curve between cluster center k and the average IoU is shown in Figure 7. It can be seen from Figure 7 that the average IoU becomes more and more stable with the increase in k values; when k = 9, the average IoU is 89.13%, and the average IoU varies slowly when the number k is bigger than 9. Finally, the clustering center k was set as 9 for dataset 'InSF-detection', and the initial anchor boxes for insulator faults detection were obtained as follows: (17, 13), (23, 15), (20, 17), (25, 17), (21, 21), (24, 19), (26, 23), (23, 26), and (30, 28), respectively.
## 3 Results  
### Media Phytotoxicity and Properties  
The water extract of the commercial propagation mix and the P had significantly higher EP than DI water (the control). All other biochar-amended mixes, the PM:P mix, and PM had a similar EP compared to the control (Figure 1).  
The pH of HB-amended mixes had positive linear correlations with the biochar incorporation rate, while SBB-amended mixes showed quadratic correlations. The electrical conductivity (EC) of all biochar-amended mixes increased with an increasing biochar incorporation rate, and had quadratic correlations (Figure 2). All of the mixes' TPs were within the recommended range (50% to 85%). The TPs of the HB-amended mixes had positive linear correlations with the biochar incorporation rate; however, the SBB-amended mixes showed quadratic correlations. All of the SBB-amended mixes' CCs were also within the recommended range (45% to 65%), except for 100% SBB mixes (71%). The CC of 10% HB-amended mixes was within the recommended range (62%), while all the other HB-amended mixes' CCs were slightly beyond the range (68% the highest). The air space of all the biochar-amended mixes was within the recommended range (10% to 30%), except for the 50%, 70%, and 100% SBB-amended mixes. The air space of all SBB-amended mixes decreased as the biochar incorporation rate increased; however, the AS of all HB-amended mixes increased with the biochar rate. The bulk density (BD) of both SBB- and HB-amended mixes were similar (Figure 3).  
Figure 1: The emergence percentage of basil seedlings in the water extract of different mixes. *, ** indicate a significant difference from the control (DI water) using Dunnett’s test at \(p\leq 0.05\) and \(p\leq 0.01\), respectively. SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar; CS = commercial propagation substrate; SBB (10%, 30%, 50%, 70% and 100%; by vol.) incorporated with 30% perlite with the rest being peat moss; HB (10%, 30%, 50% and 70%; by vol.) incorporated with peat moss.  
Figure 2: The correlation between pH **(a)** and electrical conductivity (EC) **(b)** and biochar incorporation rate. SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar. SBB (10%, 30%, 50%, 70% and 100%; by vol.) incorporated with 30% perlite with the rest being peat moss; HB (10%, 30%, 50% and 70%; by vol.) incorporated with peat moss.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 2 System Model
### Ambient Source Model
For the \(k\)-th time slot, frequency-domain ambient source signal \(\hat{\mathbf{s}}_{u,k}\in\mathbb{C}^{L\times 1}\) can be represented by  
\[\hat{\mathbf{s}}_{u,k}=[\hat{s}_{u,k}(1)\cdots\hat{s}_{u,k}(l)\cdots\hat{s}_{u,k}(L)]^{T},\quad\text{where }\hat{s}_{u,k}(l)= \begin{cases}\sqrt{\frac{(1-\zeta)L_{ \text{A}}}{L_{\text{D}}}}\sigma_{s}\rho_{\text{D}}(l,u,k),&\text{if }l\in\Phi_{\text{D}},\\ \sqrt{\frac{\zeta L_{\text{A}}}{L_{\text{D}}}}\sigma_{s}\rho_{\text{PI}}(l,u, k),&\text{if }l\in\Phi_{\text{PI}},\\ 0,&\text{if }l\in\Phi_{\text{G}},\end{cases}  \tag{1}\]  
where \(0<\zeta<1\) denotes the power allocation ratio [33] for the pilot subcarriers, \(\sigma_{s}^{2}\) the average power of the source symbols, and we assume equal power allocation for each set of subcarriers \(\Phi_{\text{PI}},\Phi_{\text{D}}\). In the next step, the frequency-domain OFDM signal \(\hat{\mathbf{s}}_{u,k}\) is transformed into the time-domain signal \(\mathbf{s}_{u,k}\) by inverse discrete Fourier transform (IDFT) followed by insertion of the cyclic prefix (CP) of length \(L_{\text{CP}}\). To do this, we define unitary IDFT matrix \(\mathbf{\Omega}\) (i.e., \(\mathbf{\Omega}^{H}\mathbf{\Omega}=\mathbf{I}_{L}\)) and CP insertion matrix \(\Xi^{+}\) as [25; 34]  
\[\mathbf{\Omega} =\{\omega_{l,l^{\prime}}\}\in\mathbb{C}^{L\times L},\quad\text{ where }\omega_{l,l^{\prime}}=\frac{1}{\sqrt{L}}e^{j\frac{2\pi}{L}(l^{\prime}-1)(l-1)}, \tag{2}\] \[\mathbf{\Xi}^{+} =[\mathbf{I}_{\text{CP}}\,\mathbf{I}_{L}]\in\mathbb{B}^{\bar{L} \times L}, \tag{3}\]  
where \(\mathbf{I}_{L}\) denotes the identity matrix of size \(L\), \(\mathbf{I}_{\text{CP}}\) is generated by picking last \(L_{\text{CP}}\) rows from the matrix \(\mathbf{I}_{L}\), and the length of the time-domain signal is given by \(\bar{L}=L+L_{\text{CP}}\)[12]. As a result, the transformed signal \(\mathbf{s}_{u,k}\) can be expressed by [25; 34]  
\[\mathbf{s}_{u,k}=\Xi^{+}\mathbf{\Omega}\hat{\mathbf{s}}_{u,k}\in\mathbb{C}^{L \times 1}, \tag{4}\]  
where the signal \(\mathbf{s}_{u,k}\) is sampled at the reader for every \(T_{c}=\frac{T_{c}}{L}\) period (chip duration).
## 4 Results and Discussion
### Validation Approach  
The effectiveness of the framework was validated using historical and real-time data collected from sensors and MES logs within the tube manufacturing facility described in Section 3. The data encompassed operational parameters (cycle times, throughput, idle times, defect rates) and environmental metrics (energy consumption, derived emissions, waste generation), providing a rich dataset for analysis and optimization.  
The process mining data transformation converted raw sensor data (32,257 rows of hourly snapshots across 123 columns) into actionable optimization inputs. This raw data were transformed into a granular event log suitable for process mining (S1). An event was generated each time a machine's status sensor changed its state (e.g., from 'idle' to 'running'). For continuous sensors like power meters, an event was recorded when the reading crossed a pre-defined operational threshold. This state-change detection approach converted the time-series data into a structured log of 391,127 discrete events (S2), each containing a CaseID, Activity, Timestamp, Resource, and Event Type.  
Table 1 presents the first 10 events from this comprehensive event log, illustrating the granular nature of the collected data.  
Figure 5: The Life Cycle Assessment (LCA) for the tube manufacturing facility.
###### Abstract  
The rapid development and availability of drones has raised growing interest in their numerous applications, especially for aerial remote-sensing tasks using the Internet of Drones (IoD) for smart city applications. Drones image a large-scale, high-resolution, and no visible band short wavelength infrared (SWIR) ground aerial map of the investigated area for remote sensing. However, due to the high-altitude environment, a drone can easily jitter due to dynamic weather conditions, resulting in blurred SWIR images. Furthermore, it can easily be influenced by clouds and shadow images, thereby resulting in the failed construction of a remote-sensing map. Most UAV remote-sensing studies use RGB cameras. In this study, we developed a platform for intelligent aerial remote sensing using SWIR cameras in an IoD environment. First, we developed a prototype for an aerial SWIR image remote-sensing system. Then, to address the low-quality aerial image issue and reroute the trajectory, we proposed an effective lightweight multitask deep learning-based flying model (LMFM). The experimental results demonstrate that our proposed intelligent drone-based remote-sensing system efficiently stabilizes the drone using our designed LMFM approach in the onboard computer and successfully builds a high-quality aerial remote-sensing map. Furthermore, the proposed LMFM has computationally efficient characteristics that offer near state-of-the-art accuracy at up to 6.97 FPS, making it suitable for low-cost low-power devices.  
aerial remote sensing; lightweight edge computing model; Internet of Drones; deep learning-based path replanning; aerial SWIR image map +
Footnote †: journal: Applied  
Based on [10], this review article suggests that performing remote-sensing tasks by using multi- or hyperspectral data and multitask learning is a novel approach. Thus, images acquired only from the RGB camera do not satisfy the requirements of drone remote-sensing applications, such as in agricultural applications [11]. Recently, there has been increasing demand for capturing no visible band images to improve the efficiency of drone remote-sensing applications. A spectral domain of interest is the short-wavelength infrared (SWIR) region situated between 1.1 \(\upmu\)m and 1.7 \(\upmu\)m [12]. The SWIR camera produces clear images in the SWIR spectrum and sees some objects that are blocked by the fog, rain, and mist. Thus, it is more resistant to fog or smoke, low light, or nighttime conditions [13]. Hence, it is useful for drone remote sensing, because the SWIR spectrum reveals many features not obtained in the visible spectrum.
# Name-Based Security for Information-Centric Networking Architectures
## 4 Name-Based Security Solutions
### Access Control  
For this solution, we leverage IB-PRE to provide access control on content items stored in network endpoints outside the administrative realm of the content owner, as in [19]. We assume that each content item is encrypted by the content owner using a symmetric encryption algorithm (each item with a different key) and each item is protected by an access control policy. Our goal is to allow authorized users to access the corresponding encryption key. Each access control policy is identified by a name similar to a content name (for example \"User A/Friends\") and it may protect multiple items. In order to achieve our goal, content owners encrypt the symmetric encryption keys with the IBE _Encrypt_ algorithm using the name of the policy as a key; we refer to the latter ciphertext as \(C_{policy\_name}(key)\). Moreover, for each authorized user, the content owner creates an IB-PRE re-encryption key of the form \(RK_{policy\_name\_user\_name}\). Finally, the \(C_{policy\_name}(key)\) ciphertexts, the re-encryption keys, and a list of authorized users (and their corresponding \(PP\)) are transferred to the content storage network endpoints. With this information, an endpoint can re-encrypt a \(C_{policy\_name}(key)\) into \(C_{user\_name}(key)\).