esa-sceva/satcom-chunk-collection huggingface dataset:
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Datasets  
The BDD100K [22] and KITTI [23] datasets were utilized as benchmarks for our object detection model. These datasets inherently present significant challenges for autonomous driving applications, such as object occlusion, small target sizes, and irregularly shaped targets (e.g., pedestrians, cyclists). A prominent issue identified was class imbalance, particularly a low 'Cyclist' proportion in KITTI, which can bias models against minority classes [61].  
To mitigate these challenges, our comprehensive preprocessing strategy involved several steps. We first removed severely blurred images (with a Laplacian variance \(<100\)[62]) and duplicates to ensure data quality. Crucially, to address the class imbalance, we performed targeted \(2\times\) instance replication for the 'Cyclist' category, significantly improving its representation and ensuring a more balanced label distribution across the dataset. The dataset partitioning followed standard guidelines: BDD100K was split into 70% for training, 10% for validation, and 20% for testing, while KITTI used an 80% training, 10% validation, and 10% testing split.  
Beyond preprocessing, data augmentation techniques (detailed in Section 4.2, Table 1), including Mosaic [14] and Copy-Paste [63], were extensively employed. These methods not only enhance model robustness but also help mitigate potential spatial biases observed in the datasets (e.g., bounding boxes predominantly clustering in the lower left due to a typical driving perspective). Our proposed model, specifically its DCNv3_LKA_C2f module and Inner-Shape IoU loss, is intrinsically designed to implicitly learn from and effectively handle these complexities and irregular object shapes present in the standard annotations of BDD100K and KITTI. While strong BDD100K performance suggests some generalization, thorough evaluation on diverse unseen data remains future work.
## 4 Machine-Learning-Aided Positioning in 5G Networks
### Lessons Learned  
The presented papers show how the emerging communication technologies (e.g., massive MIMO, mmWave communication) of 5G networks can be used for positioning with machine learning. These new technologies increase the amount of data and the number of features available for positioning. Most of these new features (e.g., AoD) can be handled with traditional techniques (e.g., triangulation), but the combination of these features and the increased amount of data make the ML techniques easily manageable compared to traditional techniques. The main advantage of ML algorithms is rapid model development and updating, as new data and measurement types can be used and combined immediately. This makes them particularly well suited as a replacement for traditional fingerprinting algorithms, especially in cases where measurements have a complex relationship to positions.  
Results show that in ideal scenarios (low noise, high number of antennas or BSs, LOS propagation), a MAE of less than 2 m can be achieved in indoor and outdoor environments. In NLOS scenarios and high noise scenarios, the MAE becomes much higher and can easily exceed 20 m. It should be noted that in most cases, positioning methods are tested in a fully or partially simulated environment, due to the lack of deployed 5G networks. Simulations tend to underestimate the effects of the physical environment, as many of these effects are difficult to estimate and depend heavily on the particular environment. Models vary greatly in several parameters, such as the number of LoS base stations, the type of simulated signal, and the size of the virtual space; therefore, a comparison of these positioning solutions based only on their positioning error could be very misleading.
## 3 Chassis Strains for 3-HP Production
### Other Chassis Strains  
In recent studies, _Bacillus subtilis_ and _Pseudomonas asiatica_ have also been used as chassis strains for the synthesis of 3-HP. Although _B. subtilis_ demonstrated the ability to rapidly take up glycerol and grow well on glycerol as the substrate, it is limited by its inability to synthesize coenzyme B\({}_{12}\). The engineered _B. subtilis_, with an introduced glycerol synthesis pathway and modulated expression of glycerol assimilation genes, produced 10 g/L of 3-HP in shake flask fermentation using glycerol as the substrate [49]. Similar to _P. denitrificans_, _P. asiatica_ C1 also naturally synthesizes coenzyme B\({}_{12}\) under aerobicconditions. _P. asiatica_ C1 was engineered for 3-HP production by overexpressing genes related to the coenzyme A-independent synthesis pathway, disrupting genes responsible for 3-HP degradation, and promoting glycerol assimilation, which resulted in a 3-HP titer of 63 g/L and a yield of 0.99 mol/mol of glycerol in fed-batch fermentation [51].  
With the development of third-generation microbial refineries, photosynthetic microorganisms such as cyanobacteria are being engineered for the production of chemicals directly from CO\({}_{2}\). However, a pathway for synthesizing glycerol and converting it into 3-HP is not available in cyanobacteria. At present, only the malonyl-CoA pathway and the \(\beta\)-alanine pathway have been reported in cyanobacteria. [PERSON] et al. [52] constructed an optimized malonyl-CoA pathway in _Synchocystis_ sp. PCC 6803 and produced 837.18 mg/L of 3-HP. Another excellent production strategy was to construct a microbial consortium consisting of cyanobacteria and _E. coli_, in which engineered _Synechococcus elongatus_ was responsible for the synthesis of sucrose from CO\({}_{2}\) and engineered _E. coli_ further synthesized 3-HP from sucrose. Unfortunately, the 3-HP titers obtained using cyanobacteria remain very poor.
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
## 3 Estimation of Translational Speed and Rotational Angular Velocity  
POS system sets inertial navigation technology and satellite navigation technology in one body, and adopts the real-time and post-process information fusion respectively to get high precision positioning and orientation information. For MSCI, the imaging time is very short, so the error accumulation of the INS is negligible, and the INS is more accurate in a short time. Hence the measured position and angular data for estimation of translational speed and rotational angular velocity are very accurate.  
The calculation of the real-time position vector of each antenna at high-speed sampling time requires the translational speed and the rotational angular velocity of the antenna array. The low-speed discrete POS data will be used to estimate the translational speed and the rotational angular velocity. Since the data rate of POS is usually less than the pulse repetition frequency as Figure 3 shows, there are many pulses between two adjacent POS data. To obtain the translational speed and the rotational angular velocity of the antenna array during each pulse, third-order polynomial curve fitting to the position and attitude is employed and the least squares method is used to obtain the coefficients of the fitting polynomial. The fitting polynomial of the position or the attitude can be expressed as  
\[\mu\left(t\right)=\sum_{k=0}^{3}a_{\mu,k}t^{k}, \tag{20}\]  
where \(a_{\mu,k}\) is the coefficient of the polynomial, and \(\mu\left(t\right)\) is the fitting curve.  
The initial time of the \(l\)-th pulse is denoted as \(t_{l,0}\), and the end time of the \(l\)-th pulse is denoted as \(t_{l+1,0}=t_{l,0}+T_{p}\). By substituting \(t_{l,0}\) and \(t_{l+1,0}\) into the fitting curve \(\mu\left(t\right)\), we can get the position or the attitude parameters at the beginning and the end of each pulse. Based on the assumption that the antenna array is uniformly translated and rotated during each pulse, the translational speed and rotational angular velocity in each pulse can be solved by  
\[\mathbf{\omega}_{l}=\begin{bmatrix}\dfrac{\theta\left(t_{l+1,0}\right)-\theta \left(t_{l,0}\right)}{T}\\ \dfrac{\varphi\left(t_{l+1,0}\right)-\varphi\left(t_{l,0}\right)}{T}\\ \dfrac{\varphi\left(t_{l+1,0}\right)-\varphi\left(t_{l,0}\right)}{T}\\ \end{bmatrix}, \tag{21}\]  
\[\mathbf{v}_{l}=\dfrac{\mathbf{r}^{\text{s}}\left(t_{l+1,0}\right)-\mathbf{r}^ {\text{s}}\left(t_{l,0}\right)}{T}. \tag{22}\]
## 2 Description of the Forward Model
### The Rayleigh Scattering and Gas Absorption Model  
Atmospheric states are represented on a 25-level vertical grid, which includes the profiles of temperature and pressure and the volume mixing ratios of trace gases (O\({}_{2}\), H\({}_{2}\)O and CO\({}_{2}\)). For CO\({}_{2}\) absorption, we divided the CO\({}_{2}\) profile into two regimes. The lower regime is from the surface to 2 km, with the rest of the atmosphere comprising the upper regime. The concentration of CO\({}_{2}\) in the lower regime changes frequently due to various sources and sinks, while little change happens in the upper regime. Three ancillary parameters are also included to correct for the effect of using climatology data; these are (1) a single temperature shift S applied to all temperature levels uniformly, (2) the surface pressure; and (3) a scaling factor, F\({}_{\text{H2O}}\), for the total amount of water vapor.  
Rayleigh scattering cross-sections and depolarization ratios are taken from [PERSON] et al. [46]. Jacobians of the Rayleigh optical depth are obtained by an appropriate differentiation.  
For trace gas absorption, spectroscopic line parameters from the high-resolution transmission molecular absorption database (HITRAN) [47] are used as an input for the line-by-line (LBL) computation of absorption cross-sections. As with the Rayleigh optical inputs, the gas absorption optical properties are fully linearized.
## 7 Results Analysis
### Simulated Results
#### 7.2.3 Charge-Discharge State  
In this state, the system is tested for real life scenarios where it switches between charging, discharging, and standby modes. The analysis of simulations is performed for a short duration of 12s so the timescale is sufficient to capture the most important effects and show the system responsiveness. In each case, the system is tested for sudden and gradual load changes and waveforms of the PMSM-Flywheel torque, three-phase current and voltage, SOC, and DC-bus voltage are plotted (Figures 14 and 15). The torque, current and power waveforms are plotted on the same axis for better comparison.  
The torque is initially at \(T=10\) N\(\cdot\)m, indicating that the flywheel is charging, and the DC-bus is supplying the FESS. At \(t=2\) s it is reduced to \(T=5\) N\(\cdot\)m, but the FESS is still charging at a constant rate. At \(t=4.5\) s, the charging is slowed down gradually until the flywheel switches to standby mode at \(t=6.5\) s. The system starts to charge again at \(t=8\) s and continue for 2 s before switching back to standby state. Due to higher rotational speed of the PMSM-Flywheel rotor, the three-phase current waveforms are not visible for analysis, and hence, the zoomed in views at \(t=2\) s and \(t=8\) s are shown in Figure 14.  
The speed waveform of the electrical machine at no-load condition (flywheel not connected) is also shown in Figure 14. The PMSM speed follows the torque, and accelerates when the torque is reduced (\(t=2\) s & 4.5 s), and slows down when the torque increases at \(t=8\) s.  
The inverter output voltage, DC-bus voltage, and SOC of FESS are presented in Figure 15. The yDC-bus voltage fluctuates between \(\pm 3\)% of the rated voltage during the step changes at \(t~{}=~{}2,~{}8,~{}\&~{}10\) s. It rises to 620 V when the torque is reduced, whereas it drops to 590 V when the torque is increased. This is due to sudden torque changes; however, these voltage variations only last for approximately 45 ms. Except during the load torque changes, the voltage output and DC-bus voltage are stable, and the flywheel charging variations follow the load when switching between different charging states.  
Figure 13: FESS in discharging mode: Flywheel stored energy (top); Flywheel SOC (bottom).
# Low-Cost Active Anomaly Detection with Switching Latency
### Contributions  
In this paper, we consider the problem with the objective of minimizing the expected cumulative costs that take into account not only detection cost but also the switching cost. We are thus facing a new problem that requires a trade-off between the cost per unit time, the sample size and the switching across processes. We propose a deterministic policy  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Without Switching Cost** & **With Switching Cost** \\ \hline Minimizing the sample size & [16; 17; 18; 19; 20] & [24; 26] \\ \hline Minimizing the cumulative costs & [21; 22; 23] & Our work \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between our work with the works in literature.  
(DMSC policy) which makes the number of switching as small as possible while ensuring the asymptotic optimality of the expected cumulative cost incurred by observations. Furthermore, we focus on the problem in the scenario where the number of anomalies is known in advance. In this scenario, to detect all the anomalies, the decision-maker can directly identify all the abnormal processes and make a declaration; or it can exclude all the normal processes and thus obtain the correct information about the anomalies. Thus, the proposed algorithm partitions the problem into two cases. In one case, the processes most likely to be abnormal will be probed with the highest probability; in another case, the decision-maker probes processes that are likely to be normal and eliminates them one by one. We analyze the asymptotic performances of the DMSC policy for different ratios of single-switching delay to single-observation delay as the error constraint approaches zero. We draw the conclusion that the proposed DMSC policy is asymptotically optimal in terms of minimizing the expected cumulative costs when the ratio of single-switching delay to the single observation delay is much smaller than the declaration threshold and is order-optimal when the ratio is comparable to the threshold. The strong performance in the finite regime is demonstrated in the simulation part.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.4 Energy-Efficient Cooperative Opportunistic Routing Protocol (E2 CORP)  
The energy efficient cooperative opportunistic routing protocol (E2 CORP) for UWSNs was introduced by the author in [150]. The Tx anchor node first chooses a set of forwarding nodes before choosing the optimal repeater node from a group of nearby repeater nodes. In order to prevent data packets from being communicated through repeater nodes that were not selected for the forwarder set, the network employs a holding time. If the E2 CORP achieves great performance in terms of energy efficiency, end-to-end delay of the network and communicating data packet delivery ratio. However, if the communicating nodes are located very far from one another, the repeater node selection technique in the network will perform poorly. Furthermore, when the communicating node positions are changed due to the ocean waves, then the sending of the communicating data packets to the chosen repeater nodes becomes a difficult task, which will cause more delays in the network.
## 3 Analysis of the Load-Bearing Characteristics of a Single Pile
### Axial Force of Steel Tube  
The steel tubes were made of Q235 hot-rolled seamless steel (Tianjin Youfa Steel Pipe Group Sales Co., Ltd., Tianjin, China). The yield strength of the steel was determined to be 235 MPa, and the elastic modulus was 2.1 \(\times\) 10\({}^{5}\) MPa through standard tensile tests of metallic materials [21]. Strain sensors were embedded in the inner wall of the steel tubes to monitor the variation of axial strain with depth during the load test. For Pile No. 1, strain sensors were embedded at depths of 1.0 m, 2.0 m, 5.0 m, 10.0 m, 19.5 m, 29.5 m, and 39.5 m below the pile head. For Pile No. 2, strain sensors were embedded at depths of 1.0 m, 2.0 m, 5.0 m, 10.0 m, 19.5 m, 25.0 m, 29.5 m, 35.0 m, and 39.5 m below the pile head. For Piles No.  
Figure 5: Load–settlement curve.  
3 and No. 4, strain sensors were embedded at depths of 1.0 m, 2.0 m, 5.0 m, 10.0 m, and 21.5 m below the pile head. The locations of the sensors are shown in Figure 6.  
Strain data were collected from strain sensors using a multi-channel static strain gauge. When calculating the longitudinal stress on the inner wall of the steel tube, the confining effect of the steel tube on the concrete was taken into account [22]. The axial force conditions of the steel tube at different positions during the test are shown in Figure 7.  
Figure 6: Schematic diagram of sensor location.  
Figure 7: Axial force of steel tube.  
For four different sizes of steel tube concrete mixing composite piles, the axial force of the steel tube decreased with increasing depth. As shown in Figure 7, for Piles No. 1 and No. 3, the axial force of the steel tube decreased significantly with depth in the lower half of the pile length. In the interval from the middle to the end of the pile, the axial force was relatively small, indicating that using a constant-diameter design in this part would lead to material waste. Comparing Pile No. 1 with Pile No. 2 and Pile No. 3 with Pile No. 4, it could be concluded that a variable-diameter design could more effectively transfer the load in the interval from the middle to the end of the pile length through the cement soil surrounding the steel tube to the soil around the pile. As the load was gradually transferred downward, it was also gradually diffused through the cement soil surrounding the steel tube to the soil around the pile, forming a double-layer load diffusion mode: diffusion from steel tube concrete to cement soil and from cement soil to the soil around the pile. This double-layer load transfer system effectively transferred the upper load to a larger range of soil. Pile No. 2 used 78% of the steel of Pile No. 1 but could achieve 93% of the load-bearing capacity of Pile No. 1. In summary, a variable-diameter design was more suitable for steel tubes. When designing variable-diameter and steel tube thickness adjustment for steel tube concrete mixing composite piles, operations in the lower half of the pile length were more effective.
# Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
## 3 Results
### Two-Population Network  
The two-population network described in Section 2.4 is designed to evaluate the scaling performance of the proposed network construction method. To this end, we perform simulations on NEST GPU (_onboard_) varying the number of neurons and the number of connections per neuron. The scaling performance of NEST GPU (_offboard_) has been evaluated on [9] for a balanced network model. We opted for a total number of neurons in the network (\(N\)) ranging from 1000 to 1,000,000 and a target number of connections per neuron (\(K\)) ranging from 100 to 10,000.  
To enable the largest networks, benchmarks are performed on the JURECA-DC cluster, which is equipped with the GPUs with the largest GPU memory (i.e., the NVIDIA A100 with 40 GB) among the systems described in Table 1. Figure 5 shows the network construction times using the fixed_total_number connection rule and ranging the number of neurons and connections per neuron. The performance obtained using the fixed_indegree and fixed_outdegree connection rules are totally compatible with the ones shown in this figure, and the respective plots are available in Appendix D for completeness.  
As can be seen, the value of network construction time for the network with \(10^{6}\) neurons and \(10^{4}\) connections per neuron is not shown because of the lack of GPU memory. Using an NVIDIA A100 GPU, we can, thus, say that this method enables the construction of networks with up to an order of magnitude of \(10^{9}\) connections. The largest network tested on this GPU comprised \(3\times 10^{5}\) neurons with \(K=10\),000 connections per neuron (data not shown).  
Figure 5: Network construction time of the two-population network with \(N\) neurons in total and \(K\) connections per neuron using the fixed_total_number connection rule, i.e., the average amount of connections per neuron is \(K\), and the total number of connections is \(N\times K\). Error bars indicate the standard deviation of the performance across 10 simulations using different seeds.
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 5 Edible Film and Coating Functionalities Important for the Small Fruit Industry
### Bioactivity of the Edible Films and Coatings
#### 5.1.3 Anti-Enzymatic Capacity
The authors observed inhibited the increase in GPOD and PPO activities; and retarded the reduction in CAT activity. [PERSON] et al. [133] also observed that the PPO activity of postharvest coatings of 33% _Aloe vera_ gel-treated grapes was reduced. [PERSON] et al. [132] studied the impact of incorporating cinnamon oil into starch-based and gelatin-based edible coatings on the level of ROS and the activity of blueberries. Fruit coated with cinnamon oil was characterized by a lower ability to produce ROS and a lower activity of antioxidant enzymes (SOD and CAT). In the case of fruit packed in coatings, the APX, GPX, and PPO activities were significantly lower than that of the control fruit. However, PAL activity, both in the control and test samples, decreased as the storage period extended. Edible coating/films can also inhibit the activity of enzymes such as cellulase, pectin lyase, and polygalacturonase, which break down the polysaccharides in fruit cell walls, thereby preventing their spoilage [173]. All of the coatings proposed by [PERSON] et al. [142] inhibited cell wall degrading enzymes: polygalacturonase (PGase) and pectin-lysae (PLase). Among the seven tested coatings, 1% chitosan, 1% starch, 0.5% sorbitol, 0.05% tween, and 0.02% thymol were the best. [PERSON] et al. [138] investigated the impact of a coating made from xanthan gum (XG) combined with sodium nitroprusside (SNP) on the antioxidant defense mechanisms of strawberries during storage. Their findings demonstrated that strawberries treated with XG 0.50% + SNP coating exhibited significantly increased selected antioxidant enzyme activities (CAT, POD, and PAL). Moreover, this coating effectively inhibited polyphenol oxidase (PPO) activity.