esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Literature Review
### Routing  
Routing is a fundamental network function in any network. Due to the multiple-hop network structure that might regularly alter as a result of mobility, routing is difficult in VANETs [28]. Greedy Perimeter Stateless Routing (GPSR), which takes routing decisions based on the immediate neighbor, is one of the routing protocols used in VANETs [29]. Further, Ad Hoc On-demand Distance Vector (AODV) routing, which does not require global periodic routing advertisements, is a distance vector routing protocol in which the routes are obtained on demand [30]. Using the concept of multipoint relays, which are the nodes forwarding broadcast messages during the flooding process, Optimized Link State Routing (OLSR) uses link states generated by the multipoint relays for routing [31]. Using periodic broadcasts to immediate neighbors, routing tables in each of the nodes are updated in the Destination Sequenced Distance Vector (DSDV) routing protocol [32].
## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems
#### 3.6.3 IMU-Vision Hybrid Systems  
[PERSON] et al. presented a study on a hybrid navigation method that combines computer vision with a set of inertial sensors to offer greater precision and efficiency in locating targets indoors [114]. The visual information is formed by a combination of two cameras (stereovision), which allows the calculation of a depth map and the distance for markers (reference points) or obstacles, in addition to reducing the errors accumulated by the gyroscope sensor. Inertial information is provided by a set of gyroscope, accelerometer, and magnetometer sensors. The Kalman dual filter (DKF) algorithm combines the data, which also reduces the accumulated error of the magnetometer. The results indicate that the method reached an average accuracy location level of 0.5 m.  
[PERSON] et al. proposed the construction of an intelligent home automation system to detect and recognize its residents through visual facial identification and tracking by a set of inertial sensors, available on smartphones [122]. The application responsible for facial recognition was run on a low-cost server, built on the Raspberry pi platform, which applied a Haar-like decision tree to extract the biometrics and compare with a photo base. Interactions with users occurred by sending audio containing instructions on the user's location within the home structure.
# Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
## 3 Materials and Methods
### DOG Generation Module
If f is the first frame, then one obtains the following: \(f^{Mask}\) = inference with SAM given \(f_{1}\); \(f^{Background}_{1}\) = inference with LaMa given \(f^{Mask}_{1}\); \(f^{Depth\ Result}_{1}\) = inference with Depth Anything given \(f^{Background}_{1}\); \(f^{Depth\ Background}_{1}\) = apply CLAHE on \(f^{Depth\ Result}_{1}\); \(Rol\_image\) = apply Otsu thresholding on \(f^{Depth\ Background}_{1}\). Elsewhere, one obtains the following: \(f^{Hue}_{n}\), \(f^{Saturation}_{n}\), \(f^{Value}_{n}\)= split \(f\) into HSV color space; \(f^{Value}_{n}\) = apply CLAHE on \(f^{Value}_{n}\); \(f^{Summed\ Image}_{n}\) = Weighted Sum (\(f^{Value}_{n}\), \(f^{Depth\ Background}_{1}\), \(\alpha=0.5,\ \beta=0.5\)); \(f^{Insert\ Saturation}_{n}\) = bitwisent \(f^{Saturation}_{n}\); \(f^{Multipl\ Image}_{n}\) = element-wise multiply(\(f^{Summed\ Image}_{n}\), \(f^{Insert\ Saturation}_{n}\)). If \(Rol[y,x]\) is 0, then one obtains the following: \(f^{multipl\ Image}_{n}[y,x]\) = copy pixel value from \(f^{Depth\ Result}_{1}\) to \(f^{Multipl\ Image}_{n}\); \(f^{DOG\ Image}_{n}=f^{Multipl\ Image}_{n}\). Return\(f^{DOG\ Image}_{n}\). ```  
**Algorithm 1** RoI Setting Module and DOG Generation Module  
In summary, the proposed methodology integrates these state-of-the-art foundation models and advanced processing techniques to address the challenges of unseen environments. The modular design of the RoI Setting Module and DOG Generation Module enables accurate separation of the foreground and background, while enhancing the generalization capabilities for object detection models. This approach demonstrates both its practicality and potential for deployment in real-world scenarios, such as pigpen monitoring systems.
# The Life-Cycle Environmental Impact of Recycling of Restaurant Food Waste in Lanzhou, China
## 3 Results and Discussion
### Assessment of Environmental Impact of Anaerobic Digestion-Aerobic Compositing Technique  
#### 3.3.1 Environmental Impact of All Phases  
As shown in Table 4, after normalizing the assessment of various types of environmental impacts, they were found to be, from greatest to least effect, global warming potential, photochemical ozone creation potential, acidification potential, eutrophication potential, marine aquatic ecotoxicity potential, human toxicity potential, freshwater aquatic ecotoxicity potential, and terrestrial ecotoxicity potential. These results indicate that, in the life cycle of restaurant food waste treatment, the greatest environmental impact comes from the emission of greenhouse gases, and the least pronounced environmental impact is the ecotoxicity potential from toxic and hazardous substances produced from restaurant food waste. The environmental impact of each phase is discussed below.  
In the collection and transportation phase, the maximum environmental impact is photochemical ozone creation potential, and the minimum is freshwater aquatic ecotoxicity potential. As shown, carbon monoxide and hydrocarbons emitted by vehicle exhaust emissions in the collection and transportation phase produce photochemical pollution. In the pre-processing phase, environmental impacts from greatest to least were found to be acidification potential, eutrophication potential, and human toxicity potential. The most pronounced environmental impact of the solid composite stage was found to be global warming potential, and the least was found to be terrestrial ecotoxicity potential. The most pronounced environmental impact of the anaerobic digestion phase is global warming potential, and the least is marine aquatic ecotoxicity potential. The most pronounced environmental impact of the biogas power generation and heating phase is global warming potential, and it has no effect on ecotoxicity potential. In the biogas slurry composting phase, environmental impacts from greatest to least are acidification potential, eutrophication potential, and human toxicity potential. The most pronounced environmental impact of the wastewater treatment stage is marine aquatic ecotoxicity potential, which has no effect on global warming potential or photochemical ozone potential. Wastewater treatment releases harmful heavy metals and inorganic substances into freshwater, the atmosphere, and soil, which can directly and indirectly affect the aquatic environment. In addition, the conversion of restaurant food waste into biogas and organic fertilizer can reduce the environmental impact of fertilizer use and coal combustion.
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 6 Conclusions  
We proposed a model assigning discrete data rate values of the possible quality variations to multi-hop WBAN sensors. The discrete data rates can be dynamically adapted to changing network conditions to maximize the total utility of the data streams. The proposed approach can continuously stream data even in a shortage of resources, and it can also exploit the full capabilities of the devices if there are enough resources. We defined the formal model of the problem. We introduced two knapsack-problem-based algorithms to solve the problem of the sensor data rate adaptation. Our test results show that although the dynamic programming method can provide the best utility, the greedy method can also approximate the optimum well: it achieved at least 96% of the optimum. The running time of the greedy method was much shorter (1-2% of the running time of DP), which may represent a significant advantage in WSNs with limited computing capacity. The results show the importance of applying the discrete model. Although the solution of the continuous model can be adapted to the discrete case by rounding, the accuracy of this approach is low, even if the continuous model is solved optimally. The error of rounding the optimal continuous solution was, at most, 35%.  
Figure 6: The ratio of test cases with the exact optimum.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **5** & **10** & **15** & **20** & **25** \\ \hline \(1\) & 8 & 14 & 26 & 32 & 30 \\ DP & 485 & 1893 & 2395 & 2870 & 3198 \\ ILP & 28,206 & 14,709 & 14,877 & 14,201 & 44,595 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running times (in \(\upmu\)s) of the proposed data rate adaptation algorithms.  
The proposed model has some limitations. For example, the utility function is hard to define exactly. An extensive analysis would be required to better understand the utility of the data streams. In the future, the model could be extended with energy constraints, which may represent a crucial limitation in WSNs.  
This research received no external funding.  
* [1]**Data Availability Statement:** Data sharing is not applicable.  
The author declares no conflict of interest.
# Robot Swarms Decide under Perception Errors in Best-of-N Problems
## 5 Results
### Consensus Accuracy  
#### 5.2.1 Experiments Set I: Measurement Errors Can Occur Only at Site A  
For this set of experiments, we select the swarm size of 210 robots. Figure 4 depicts the evolution of collective decision over time for the configuration of equal populations--i.e., \(\{70,70,70\}\). In Figure 4, we show the results when the basic behavioral model is applied, while in Figure 4, we show the results when the distance-based algorithm for robot classification is used (when the extended model is applied). We select the distance-based algorithm as it is the one with the highest performance in terms of classification accuracy for most swarm configurations. When using the distance-based algorithm, the opinions exchanged are weighed based on the robot's class, i.e., the opinion of a robot that has classified itself as accurate weighs twice higher than an opinion of a robot that has classified itself differently. As we can see from Figure 4, the speed to achieve a consensus drops when applying the distance-based algorithm (approximately 4000 vs. approximately 5500 ts). This is due to the time spent by the robots in the classification phase (see Figure 2). In terms of decision accuracy, the basic behavioral model was able to achieve 66.67% decision accuracy without exploiting any robot classification, whereas the distance-based algorithm achieved 77% decision accuracy with the help of classifying robots and assigning a higher weight to the opinion of accurate robots.  
Next, we examine the accuracy of the collective decision for different compositions of the swarm of 210 robot. The results are reported in Table 3 as the percentage at which the swarm committed to any of the two sites. Site B (green) has a higher quality value than site A (red), as we can see in Table 1. As we can notice from the results summarized in Table 3, for both the basic behavioral model and the distance-based algorithm, the swarm  
Figure 4: The emergence of the collective decision: (**a**) when robots follow the basic behavioral model and (**b**) when robots classify themselves using the distance-based algorithm. We simulate a swarm of 70 objective, 70 underestimating, and 70 overestimating robots. Errors in measurements can occur at site A (red) only.  
converges on the site with the highest quality value (i.e., site B) in one of the following cases: (a) the overestimating population is absent; hence, no robots will overshoot the quality value of site A and, therefore, site B, which has in fact a higher quality value that dominates. (b) The overestimating population is smaller than the summation of the underestimating and the accurate populations. In such cases, the decision accuracy drops in comparison to (a); however, it stays above 50% for both approaches (with and without classification). For the cases that we tested and where the overestimating population is larger than the summation of the underestimating and accurate populations, the distance-based algorithm outperformed the basic behavioral model in terms of decision accuracy and stays about 50%.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### In-Pipeline Leak Monitoring  
For problems created inside the pipeline, several in-pipeline monitoring applications have been stated in the literature. Such problems resulting in leakages may be caused by sudden changes in pressure, corrosion, cracks, bad workmanship, defects in pipes or lack of maintenance [52]. Since the detection system comes closer to the location where the leakage is inside the pipeline, the in-pipeline leakage detection methods are considered more accurate and less sensitive to external noise. The Smartball [53] as a mobile sensor device can be used to detect and locate small leaks inside pipelines that are larger than 6 inches in diameter. The sensor device is developed in the form of a free swimming device consisting of a porous foam ball that envelopes an aluminium sphere containing the sensitive acoustic instrumentation. Another in-pipeline monitoring device, Sahara [54], is able to estimate and locate the leak in large diameter water pipelines. The system travels by the flow of the water and, in case of a leak, the exact location is marked on the surface by an operator that follows the machine movement. Both Sahara and Smartball are passive since they cannot be actuated and cannot actually maneuver inside the pipeline. Several crawlers have also been reported that utilize wheeled platforms, cameras, and a mechanism for control and communication, e.g., the _MRINSPECT_[55].  
For in-pipeline leak detection involving gas, the Explorer [56] has been used which is a long range leak inspection robot controlled by a human operator _via_ wireless RF signals. The explorer constantly looks into an installed camera to search for leaks that are useful for offline inspection. For oil pipelines, nondestructive inspection is the most successful technique. Magnetic flux leakage based detectors and ultrasounds are used quite commonly to search for pipe defects. Major fault with such an approach is the dependency on pipe material and high power usage. These are also not suitable for long range mission where maneuvering capabilities are difficult due to large pipeline size. PipeGuard is able to detect leaks in a more reliable and autonomous fashion as compared to passive approaches [52]. The PipeGuard system is inserted into the network through special insertion points and the system reports leakages wirelessly through relay stations. Leakages are detected by identifying pressure gradient in the leak's vicinity.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 1 Introduction
In lieu of interpolating our observed temperature data, we employed a satellite pixel-based modeling approach similar to that of Land Use Regression for the creation of a continuous surface of predicted temperatures across our study areas. This method (described in detail in Section 3) used continuous raster-based LULC descriptors in combination with our temperature observations to build a model, which in turn was reapplied to our LULC descriptors to create a continuous surface of predicted temperatures for an entire urban area. Besides overcoming the errors and assumptions associated with spatial interpolation schemes, we noted that a major benefit of the model-based approach taken here was the ability to assess model diagnostics for strength of fit to observations (i.e., variance explained, expected error). Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify heat? We applied our analysis to the cities of Baltimore, Maryland (MD), and Richmond, Virginia (VA), and the District of Columbia (D.C.) using ground-based temperature data collected on days when air temperatures were well above the 90 th percentile of historic averages.
## 4 Numerical Results and Discussions
### Simulation Setup and Parameters  
The effectiveness of our proposed scheme is evaluated through experimental simulations in which the full-band urban indoor communication environment is taken into account. Let \(d_{i}\) be the distances between the source \(S_{i}\) and relay \(R\), let \(d_{iu}\) be the distances between the source \(S_{i}\) to primary user PU and let \(d_{ru}\) be the distances between relay node to PU, respectively. We consider \(h_{i}=d_{i}^{-m}\), \(l_{i}=d_{iu}^{-m}\) and \(l_{r}=d_{ru}^{-m}\) as the channel gains between source \(S_{i}\) and relay \(R\), between source \(S_{i}\) and the PU and between the relay node and the PU, respectively, where \(m=2\) is the pass loss exponent. The channel gains between two nodes are reciprocal. To simplify the analysis, we consider a case where the sources and the relay are on a straight line, with \(d_{1}+d_{2}=10\) m and \(d_{1u}=d_{2u}=5\) m. The interference effects from the PU to each secondary users are assumed to be the same, i.e., \(\sigma_{u,r}^{2}=\sigma_{u,i}^{2}=\sigma_{u}^{2}\). The noise power is given as \(\sigma^{2}=10^{-9}\) W. Since full-band communication is considered, the frequency band is normalized as \(B=1\). The parameters used in the simulation part are listed in Table 1.
## 2 Principle and Structural Design of the Active Support System for a 4m SiC Primary Mirror  
The active support system for a 4m SiC primary mirror is shown in Figure 1. The system used pneumatic surface control actuators and a six-hardpoint positioning mechanism to connect the primary mirror and mirror cell. The actuators and hardpoints were epoxy-bonded to the backface of the primary mirror, and the bonding position was determined with a laser tracker. The pneumatic actuators were used to correct the surface shape of the primary mirror and the six-hardpoint positioning mechanism was used to define the relative pose of the primary mirror and mirror cell. The primary mirror was a lightweight mirror weighing 1600 kg, with a diameter of 4 m and center hole diameter of 0.5 m. It was composed of SiC and had the structure of a semienclosed honeycomb. The primary mirror had a surface thickness of 15 mm and radial and circumferential rib plates arranged on its back, forming numerous fan-shaped lightweight holes. The rib plate thickness was 7 mm (some rib plates were 12 mm thick).  
Figure 1: Structural schematic of the active support system for a 4m SiC primary mirror.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 2 Preliminaries
### Discrete Constellations and Amplitude Shaping  
We consider \(2^{m}\)-ary amplitude-shift keying (ASK) alphabets \(\mathcal{X}=\{\pm 1,\pm 3,\ldots,\pm(2^{m}-1)\}\), which can be factorized as \(\mathcal{X}=\mathcal{SA}\). Here \(\mathcal{S}=\{\pm 1\}\) and \(\mathcal{A}=\{1,3,\ldots,2^{m}-1\}\) are the sign and amplitude alphabets, respectively. The cardinality of the amplitude alphabet is \(n_{a}=|\mathcal{A}|\). Motivated by the fact that the capacity-achieving distribution for the AWGN channel is symmetric around zero, we restrict our attention to the amplitude distribution \(P_{A}(a)\), and assume that the sign distribution \(P_{S}(s)\) is uniform and independent of the amplitudes. The distribution of the channel input \(X=SA\) is then \(P_{X}(x)=P_{S}(s)P_{A}(a)\).  
The distribution that maximizes the MI for ASK constellations subject to an average power constraint does not have a known analytical form. Instead, Maxwell-Boltzmann (MB) distributions  
\[P_{A}(a)=\left\{\begin{aligned} K\left(\lambda\right)e^{- \lambda a^{2}},&\text{for }a\in\mathcal{A},\\ 0&,&\text{otherwise},\end{aligned}\right. \tag{2}\]  
are used for shaping amplitudes, e.g., in [21; 28]. As shown in (Table 5.1 in [80]), the difference in MI for the MB distribution and the capacity-achieving distribution is insignificant for ASK constellations. In (2), \(\lambda\) determines the variance of the distribution while \(K(\lambda)\) normalizes it.  
Similarly, SpSh is also employed for amplitude shaping in the discrete domain [22; 23]. In [54], it is shown that when an \(n\)-spherical region of \(\mathcal{X}^{n}\) is used as the signal space, the average distribution over \(\mathcal{A}\) approaches an MB distribution as \(n\to\infty\). The authors of [64] showed that at finite \(n\), SpSh minimizes the informational divergence between the average distribution and an MB distribution.  
To employ high-order modulation formats such as \(2^{m}\)-ASK for \(m\geq 2\), a binary labeling strategy is necessary. A discussion on binary labeling can be found in (Section 2 in [8]). We assume that the binary label \(B_{1}B_{2}\ldots B_{m}\) of a channel input \(X\) can be decomposed into a sign bit \(B_{1}\) and amplitude bits \(B_{2}B_{3}\ldots B_{m}\). In this paper, we assume that binary reflected Gray codes (BRGCs) are used for labeling (Defn. 2.10 in [8]).  
**Example 1** (Binary labeling).: _The BRGC is tabulated for 8-ASK in Figure 2 (bottom). Here, \(B_{1}\) is symmetric around zero. Furthermore, when \(X\) has a distribution which is symmetric around zero, \(B_{1}\) is uniform and stochastically independent of \(B_{2}\) and \(B_{3}\)._