esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper delves into an interference analysis, focusing on the forthcoming Starlink Generation 2 satellites, stated to operate within the 1990-1995 MHz frequency band. The aim is to assess the potential interference from this Starlink system to the satellite receivers of mobile satellite systems (MSSs), which are set to function within the 1980-2010 MHz range, and satellite receivers of the NTN systems, which are planned to operate in the n256 bands, defined by the 3 GPP specifications. Through simulation-based evaluations, both single-entry and aggregate interference levels from Starlink to MSSs and NTN systems are comprehensively explored. To estimate the interference impact, several protection criteria were used. The study is in line with the Recommendations of International Telecommunication Union (ITU-R) and common approaches that are used when performing compatibility studies between satellite systems. The findings of this study demonstrate the feasibility of utilizing the n25 band for NTN direct-to-device services.  
D2D; 5G; LTE; NTN; direct-to-device; Starlink; MSS; interference analysis; n25; n256; 3 GPP 2024, 24, 1297. [[https://doi.org/10.3390/s24041297](https://doi.org/10.3390/s24041297)]([https://doi.org/10.3390/s24041297](https://doi.org/10.3390/s24041297))
# Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast
## 3 Results
### Variability of Water Properties at Site and Sub-Site Levels  
For temperature, intraclass correlation for site in the intercept-only model was 0.01 (Table 4). This indicated that only 1% of the variance in temperature was at the site level. However, the intraclass correlations for other response variables suggested that 34, 12 and 17% of variation was at the site level for salinity, pH, and chlorophyll-a, respectively (Table 4). This meant that the overall level of these three variables differed notably between sites. Intraclass correlation for the nested site-sub-site structure was zero across all models (Table 4). This means that there was no significant variance at the sub-site level. Therefore, an appropriate random model structure included random effects only for site. Even though  
Figure 3: Interactive effects of “week” and “depth” on (**A**) temperature, (**B**) salinity (specific conductance), (**C**) pH and (**D**) chlorophyll-a concentration based on the observation-level model. See Table 3 for the corresponding model structures and coefficients. All variables are shown on back-transformed scales and observations as grey dots.  
the ICC for temperature was low, a mixed-effects model appropriately dealt with the nested data structure and was therefore also seen as the best model structure for temperature.
## 3 Results and Discussion
### Spatial and Temporal Analysis of the Effect of Climate on NDVI  
From 1982 to 2010, the effect of AMT on NDVI showed no significant change (Figure 3A). In contrast, the effect of AMP on NDVI presented a significant decreasing trend (\(R^{2}\) = 0.11, \(p\) < 0.05, Figure 3B). From the spatial scale, with the increase in AMT, the effect of AMT on NDVI decreased significantly (\(R^{2}\) = 0.31, \(p\) < 0.0001, Figure 3C). Similarly, with the increase in AMP, the effect of AMP on NDVI also presented a decreasing trend (\(R^{2}\) = 0.10, \(p\) < 0.005, Figure 3D). Why did climate generate a greater effect on NDVI in dry regions than in wet ones? Here, we proposed three explanations. First, in dry regions, temperature and precipitation are the main factors that determined the growth of vegetation [28]. When the temperature is below 0 \({}^{\circ}\)C and coupled with low precipitation, the growth of plants would tend to stop [29]. However, once the temperature is above the plant growing threshold and coupled with an increase in precipitation, the priming effect of climate on plants would promote their growth [14]. Secondly, arid regions are associated with low ecosystem stability due to their low species diversity and hostile environment [30; 31]. Therefore, the plants in dry regions are more sensitive to the dynamic of climate than those in wet regions. Thirdly, the little effect of climate on NDVI in wet regions (Figure 3C,D) might be because the growth of plants was more limited by soil resources than by climate in humid environments [32]. For instance, both nitrogen and phosphorus additions significantly promoted the biomass of grass in humid environments (e.g., AMP = 560 mm in Haibei [33], AMP = 620 mm in Maqu [34], and AMP = 747 mm in Hongyuan [35]). However, the increase in precipitation had a nonsignificant  
Figure 2: Spatial patterns of AMT (**A**), NDVI (**B**), AMP (**C**), and correlation between NDVI and temperature (**D**) and precipitation (**E**) in 1982. AMT, AMP, and NDVI represent the annual mean temperature, annual mean precipitation, and the normalized difference vegetation index, respectively.  
effect on biomass in Hongyuan [36]. Meanwhile, in areas with concentrated precipitation, excessive precipitation during the rainy season may lead to soil erosion and nutrient loss [37]. Therefore, the effect of climate on NDVI in the wet regions was lower than that in the dry regions.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.5 Plasticized Solid Polymer Electrolytes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrolyte System** & **Preparation Method** & **Conductivity, \(\sigma\) (S cm\({}^{-1}\))** & **Important Findings** & **Reference** \\ \hline  & & & Bandgap energy = 5.63 eV (indirect) & \\  & & & and 5.80 eV (direct) & \\ PEO-LiPF\({}_{6}\)-EC & Solution casting & 2.06 \(\times\) 10\({}^{-4}\) (25 \({}^{\circ}\)C) & Unplasticized SPE: & \\  & & & \(\sigma\) = 4.10 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\) (25 \({}^{\circ}\)C) & \\  & & & Bandgap energy = 5.74 eV (indirect) & \\  & & & and 5.90 eV (direct) & \\ \hline  & & & PGMA was synthesized via & \\  & & & photo-polymerization route. & \\  & & & \(E_{A}\) = 0.21 eV & \\  & & & Electrochemical stability window & \\ PGMA-LiClO\({}_{4}\)-EC & Solution casting & 2.00 \(\times\) 10\({}^{-4}\) (RT) & Unplasticized SPE: & \\  & & & \(\sigma\) = 4.20 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\) (RT); & \\  & & & \(E_{A}\) = 0.26 eV & \\  & & & Electrochemical stability window & \\  & & & 2.50 V & \\ \hline PEO-LiClO\({}_{4}\)-SN & Solution casting & \(\sim\)9.12 \(\times\) 10\({}^{-5}\) (20 \({}^{\circ}\)C) & \(E_{A}\) = 0.45 eV & \\  & & & Unplasticized SPE: & \\  & & & \(\sigma\) = 3.79 \(\times\) 10\({}^{-7}\) S cm\({}^{-1}\) (20 \({}^{\circ}\)C) & \\  & & & \(E_{A}\) = 0.89 eV & \\ \hline HEC-LiCF\({}_{3}\)SO\({}_{3}\)- & & & & \\ glycerol [EO]/[Li] = 6:1 & & & & \\  & & & & \\ Chitosan-LiClO\({}_{4}\)- & & & & \\ glycerol & Solution casting & \(\sim\)3.00 \(\times\) 10\({}^{-5}\) (25 \({}^{\circ}\)C) & \(\sigma\) = 1.40 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\) (25 \({}^{\circ}\)C) & \\  & & & & \\  & & & \(t_{in}\) = 0.955 & \\  & & & \(D\) = 4.19 \(\times\) 10\({}^{-9}\) cm\({}^{2}\) s\({}^{-1}\); & \\  & & & \(\mu\) = 1.63 \(\times\) 10\({}^{-7}\) cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\); & \\ glycerol & Solution casting & 1.20 \(\times\) 10\({}^{-3}\) (25 \({}^{\circ}\)C) & \(n\) = 4.57 \(\times\) 10\({}^{22}\) cm\({}^{-3}\) & \\  & & & Specific capacitance = 98.99 F g\({}^{-1}\) at & \\  & & & scan rate 10 mV s\({}^{-1}\) & \\ \hline PVB-LiClO\({}_{4}\)-PEG\({}_{400}\) & Solution casting & 2.15 \(\times\) 10\({}^{-6}\) (RT) & Unplasticized SPE: & \\  & & & \(\sigma\) = 5.80 \(\times\) 10\({}^{-10}\) S cm\({}^{-1}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Examples of plasticized SPEs systems found in the literature.
## 4 Experiments and Discussion
### Comparative Experiments of Different Methods
The reasons for this can be summarized in the following two points: firstly, the adversarial learning-based training strategy can improve the ability of image reconstruction; second, the regional analysis module can remove a majority of falsely detected background regions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method** & **Recall** & **Precision** & **F1-Measure** \\ \hline \multirow{4}{*}{(a)} & PHOT & **1.0** & 0.0244 & 0.0459 \\  & AnoGAN & 0.9200 & 0.0824 & 0.1513 \\  & MS-CDAE & 0.9200 & 0.2460 & 0.3882 \\  & Ours & 0.8400 & **0.7119** & **0.7706** \\ \hline \multirow{4}{*}{(b)} & PHOT & 0.3761 & 0.1405 & 0.2046 \\  & AnoGAN & **0.8906** & 0.4337 & 0.5833 \\  & MS-CDAE & 0.8303 & 0.7553 & 0.7911 \\  & Ours & 0.7422 & **0.9340** & **0.8271** \\ \hline \multirow{4}{*}{(c)} & PHOT & 0.2145 & 0.1556 & 0.1804 \\  & AnoGAN & **0.5685** & 0.1736 & 0.2660 \\ \cline{1-1}  & MS-CDAE & 0.3880 & 0.4195 & 0.4031 \\ \cline{1-1}  & Ours & 0.3236 & **0.8354** & **0.4665** \\ \hline \multirow{4}{*}{(d)} & PHOT & **0.9981** & 0.3577 & 0.5267 \\  & AnoGAN & 0.6479 & 0.4955 & 0.5616 \\ \cline{1-1}  & MS-CDAE & 0.5817 & 0.9061 & **0.7085** \\ \cline{1-1}  & Ours & 0.5214 & **0.9085** & 0.6625 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparative results of Figure 7a–d between different methods. Figure 9: Segmentation results of defective samples with three kinds of textural background from MVTec Anomaly Detection dataset under different methods. Each row from left to right are original defect image, ground truth image, results derived from PHOT, AnoGAN, MSCDAE, and our methods. (**a**) leather; (**b**) tile; (**c**) wood.
## 5 Discussion  
### Palaeodepositional Environments  
#### 5.1.1 Palaeod bathymetric Characteristics  
The migration and enrichment patterns of both Rb and K elements in water are closely related to those of clay, but Rb is more easily transported by adsorption on clay compared to K. Therefore, Rb/K values are often used to indicate changes in water depth, with larger values revealing a deeper body of water. Zr is a land-friendly element, and the farther away from the terrestrial source area, the lower the elemental content. The distribution of Zr in sedimentary rocks is dominated by the element Al, so Zr/Al values can represent changes in the land-source component of proximity transport and the depth of the water column, with smaller values indicating greater distance offshore and deeper  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Mt Raman** &
\begin{tabular}{c} **Raman** \\ **Shift/cm** \\ \end{tabular} & \(\text{d}(\text{G}-\text{D})\)/cm** & \(\text{Ro}_{\text{Rmc}}\)/\% & \(\text{Ro}_{\text{reality}}\)/\% \\ \hline \multirow{3}{*}{X-20-1} & D & 1347.4 & \multirow{3}{*}{250.6} & \multirow{3}{*}{2.25} & \multirow{3}{*}{2.01} \\  & G & 1598 & & & \\ \cline{1-1} \cline{3-5}  & D & 1334.7 & & & \\ \cline{1-1} \cline{3-5}  & G & 1599.5 & & & \\ \hline \multirow{3}{*}{X-22-1} & D & 1339.4 & \multirow{3}{*}{258.6} & \multirow{3}{*}{2.67} & \multirow{3}{*}{2} \\  & G & 1598 & & & \\ \cline{1-1} \cline{3-5}  & D & 1336.7 & & & \\ \cline{1-1} \cline{3-5}  & G & 1598 & & & \\ \hline \multirow{3}{*}{Y-14-2} & D & 1342.2 & \multirow{3}{*}{257.5} & \multirow{3}{*}{2.62} & \multirow{3}{*}{2.03} \\  & G & 1599.5 & & & \\ \hline \multirow{3}{*}{Y-5-3} & D & 1342.3 & \multirow{3}{*}{257.2} & \multirow{3}{*}{2.6} & \multirow{3}{*}{2.32} \\  & G & 1599.5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Partial Raman spectral parameters and specular body reflectance of Permian shale in Northeast Sichuan Province.  
water bodies. In the intersection map (Figure 6a), shale samples from the Dalong Formation are mainly concentrated in the lower right area, indicating that the Dalong Formation has larger Rb/K values and relatively smaller Zr/Al values, which suggests that the palaeohydrological body of the Dalong Formation is deeper compared to the Wujiaping and Maokou Formations. Meanwhile, the Zr/Al values of the Wujiaping Formation are larger and the Rb/K values are smaller, indicating that the palaeohydrology of the Wujiaping Formation is shallower. Comprehensive analysis shows that the depth of the Dalong Formation is the deepest, followed by the Maokou Formation, and the shallowest is the Wujiaping Formation; the analysis also indicates that the depositional periods of the Dalong and Maokou Formations were farther offshore, and the Wujiaping Formation was closer offshore.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 3 Proposed Technique
### New Voter Design  
In this subsection, we present our voter design. Even though we demonstrate the proposed technique using SRAM-based PUFs, the technique is generic to use with other classes of Weak PUFs to improve their reliability. The proposed voter is based on an UP/DOWN counter (UDC). A simple counter starts at an initial value of count 0 and counts upwards. By contrast, the \(n\)-bit UP/DOWN counter used in this design starts at an initial value of \((2^{n}-2)/2\). The counter value is incremented if the response from the current trial is 1, else (0) it is decreased. When the counter reaches a terminal value of 0 (or \(2^{n}-2\)), the counter _saturates_ and retains the terminal value.  
The complete setup of the proposed voter scheme along with the PUFs is shown in Figure 3. The output of the PUF cell is used as input to the \(n\)-bit UP/DOWN counter. In the figure, we show a setup where an UP/DOWN counter is shared with four PUF cells, as an example. Starting at an initial counter value of \(k=(2^{n}-2)/2\), multiple trials are conducted until the \(n\)-bit UP/DOWN counter reaches terminal values of 0 or \(2^{n}-2\). Unlike TMV, where the number of trials is _fixed_, trials may continue _indefinitely_ in an UP/DOWN counter until a terminal value is reached. When the counter reaches a terminal value of 0 (or \(2^{n}-2\)), the value of the PUF cell is resolved as a _logic-0_ (or _logic-1_). When a terminal value is reached, the trials are terminated, otherwise, in practice, the trials are continued for a pre-determined number of times. It is possible that no decision can be reached whenthe trials are terminated. The optimal values for \(n\) for varying error rates will be discussed in greater detail in Section 4.3. Multiple PUF cells can share a single UP/DOWN counter or each cell can be assigned an UP/DOWN counter, as done in previous designs [10]. The multiplexer is appropriately chosen (4:1 in Figure 3). As expected, sharing the counter across multiple cells would increase the run-time but reduce the area overhead.
# Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp
## 2 Related Works
Early CNN image classification algorithms were limited to categorize images, this leads to the labeling of certain garbage datasets such as TrashNet [22], Waste images, and Open Litter Map [23] with only the category of the garbage and its presence on a plain background. Extended TACO [8], Trash-ICRA19 [24], Drinking waste [22] are examples of datasets used for garbage target detection level, for labeling the class and bbox of all targets in the image against a complex background. However, the location of garbage class, without the precise edges of the garbage in the scene, is inconvenient for network to subsequent operations like grabbing or sucking. These issues may be solved if the masks of the garbage instances could be acquired. Therefore, the dataset used for garbage instance segmentation needs to be manually labelled with the class and mask of the garbage instance in the complex background, with the minimum enclosing box of the mask as the bbox of the instance. Table 1. lists some of the current publicly available datasets with instance masks, where Wade-AI [23], UAVVaste [6], MJU-Waste [25] and Cigarette butt tag all instances of trash in an image as a single object (rubbish or cigarette). TrashCan [26] dataset contain undersea trash, flora and fauna, with eight trash-related categories labelled. TACO [8], a consecutive growing free open dataset, contains 1500 high resolution images in diverse environment annotated with 4783 instances and divide them into 60 categories of litter which belong to 28 top classes. TACO offers a substantially more detailed range of garbage image scenes and annotated instances than the previous five datasets, making it more suitable to model learning and prediction of garbage instances in complicated contexts. There are several public garbage data sets, as shown in Table 1, that can be used for pre-training deep models, and the aforementioned associated research work [28; 29], such as the instance segmentation model [18; 19], provides the theoretical foundation and previous knowledge for garbage identification and segmentation. These models, data sets, and point cloud processing techniques can all be used together to quickly and accurately identify garbage objects in an outdoor environment.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 3 Buffer Management
### Utility Value Calculation  
Since nodes are mobile devices carried by individuals, we define the node buffer size as restricted in the opportunistic network. A node must choose which messages to delete when the buffer is full. Normally, we drop the message when the TTL elapses [8]. In this article, we define a utility value according to information of the message and the node. The sort of the message in the node is based on its utility value, and the utility value of the node is determined by all messages in node. We quantify the utility value according to the size of the message, the number of forwarding hops from the source node to the current node, and the TTL, and sort all the messages ascending in the node. As shown in Equation (1), the utility value of the message \(i\) is  
\[D(S,H,T)=\alpha\times\frac{s_{i}}{S}+\beta\times\frac{h_{i}}{H_{av}}+\gamma \times\frac{t_{i}}{TTL}, \tag{1}\]  
where \(\alpha\), \(\beta\), \(\gamma\) are the weighted factors, which represent the impact of message size, the number of forwarding hops and TTL of the message, respectively. Furthermore, \(\alpha+\beta+\gamma=1\). In addition, \(S\) is the buffer size of the node and \(H_{av}\) is the average number of hops for all messages in a node.  
According to Equation (1), \(D(S,H,T)\) is determined by the attribute value of the message itself. The existing studies [16] show that the message delivery ratio increases as the survival time of the message and the number of forwarding hops of the message increase. Therefore, if the message has the above conditions, the likelihood of the destination node successfully receiving the message increases, and we delete these messages first. Since the messages in the buffer are sorted from small to large according to their utility values, we preferentially delete messages with large utility values. To minimize the utility value of the message in the network, we define the negative correlation of the utility value of message \(i\) as  
\[U_{i}=-D_{i}. \tag{2}\]  
Figure 2: The process of the Combinational Buffer Management (CBM) queuing strategy.  
In the following, we sort the messages in descending order in the node buffer based on the negative correlation of the node utility values. We store the message with a larger negative correlation of utility values and delete or transfer the message with a smaller negative correlation of utility values.  
The utility value of the node is determined by the average utility value of all messages in the node. Therefore, the utility value of node \(M\) is  
\[U_{M}=\frac{\sum_{i=1}^{n}-D(S,H,T)}{n}, \tag{3}\]  
for which \(n\) is the number of all messages in a node.
## 4 Experimental Results
### Quantitative Comparison  
In the quantitative comparison, our method achieved the best compensation quality image measured by \(Q^{B+T}\). The reinforcement of \(T\) was closer to the non-shadow area while the average brightness was slightly lower than that of the non-shadow area; and the total compensation quality was much better than the other methods as well. Figure 11 shows the 25 images that were tested and compares the average of \(\left(\Delta B\right)^{2}\), \(\left(\Delta T\right)^{2}\), and \(Q^{B+T}\) they achieved. The results were analyzed by combining visual and quantitative outcomes. The average \(Q^{B+T}\) of our method in Figure 11 and Table 1 were 0.0038 and 0.0021, respectively. Both values indicate the same conclusion, namely, that our method produced the best compensation results, followed by the HMT method with 0.0099, and then the LCC with 0.0268. The OWC method attained the worst results due to its low capability in enhancing contrast. The other methods were not very stable. Since the \(Q^{B+T}\) cannot reflect the color deviation and the even contrast detail in a single shadow area, the methods' results differed visually, as shown in Figures 4-10.  
In summary, the comparative results indicate that our method enlarged the shaded information from low quality to a quality that was close to the non-shaded area in brightness and contrast in a more balanced way. Additionally, our method recovered the color information and self-adaptively constructed the compensation model, resulting in more even enhancement and better recovery of the original information of the shadow objects.
## 6 Simulation Results  
### Simulation Environment and Settings  
The simulation was performed on an average computer with Intel i7-13700H (Intel Corporation: Santa Clara, CA, USA) at 2.40 GHz and 16 GB RAM. All the considered algorithms were implemented in MATLAB 2023b. In our simulation, there are seven mobile devices, three edge servers, and one cloud server. We set \(\epsilon=0.4\), \(c=0.935\) watt, \(P~{}=~{}0.944\) watt, \(S_{n}=[4,80]\) Mb, \(O_{j}=I_{j}=[0.1,2]\) Mb, \(C_{C}=C_{EN}=\frac{1}{2}C_{EC}=50\) cycles/bit, and \(f_{C}=2f_{EN}=10f_{EC}\), where \(C\), \(EN\), and \(EC\) denote the cloud server, edge server, and mobile device, respectively. Additionally, we set \(B=\bar{B}=10^{6}\) Hz, \(\sigma^{2}=\bar{\sigma}^{2}=10^{-10}\) watt, and \(f_{EN}=10\) GHz [9]. The processing cost for the mobile device is denoted as \(P\), while the processing cost at the edge server is between 4 watt, and at the cloud server is 10 watt [39]. To better reflect the distance differences between different execution nodes, we set the distance between mobile devices to be 30 m, the distance between mobile devices and edge servers to be 100 m, the distance between edge servers to be 150 m, and the distances from mobile devices and edge servers to the cloud server to be 500 m and 400 m, respectively. Current research methods are predominantly based on deep learning or solved through heuristic algorithms [10; 30; 31], which cannot guarantee finding an approximate or optimal solution. Therefore, we do not compare our method with them. To evaluate the performance of the proposed algorithm, we compare it with the following methods:  
* Offloading only to servers (OOS): The BFF algorithm offloads tasks only to edge and cloud servers. If the final offloading scheme fails to meet the cost constraints, a significant amount of time will need to be spent on the local device to cache the services required to execute these tasks. Note that the BFF algorithm in this paper is primarily used for comparison with approximation algorithms, and it also performs well in certain scenarios.
* FixDoc [19]: The algorithm is presented as Algorithm 1 in [19]. It is a task offloading method based on DP, which is the earliest proposed method in the problem we are considering. We use this algorithm to derive the task offloading strategy that minimizes the cost while satisfying the cost constraint.
* Hermes [32]: A polynomial-time approximation algorithm used to solve the delay-minimization problem under the cost constraint. To ensure fairness, we impose a constraint of service caching on the original algorithm.
* Brute force: This algorithm enumerates all possible offloading strategies to obtain the optimal solution.