esa-sceva/satcom-chunk-collection huggingface dataset:
# mPrivacy: A Privacy Policy Engine and Safeguard Mechanism in Mobile Devices
## 4 Approach
### System Architecture  
In our system, the policy manager realizes the user's privacy decision into the privacy policy. There is also a privacy service and policy database for further functions.  
Figure 3 is an overview of our system architecture. From the entities' point of view, the policy manager interacts with the user at the application layer. The privacy service monitors and controls the API usages of the application. The data in the policy database are used to help the privacy service implement policy enforcement. From the perspective of the workflow, first, the privacy service monitors and collects the API usages of each application. The privacy service can obtain the information that the application wants to access from the usages of the API. With the help of the IEG in the policy database, the privacy service learns the set of accessible or inferable information from the set of resources that the application is allowed to use. When an application attempts to access certain resources that result in access to prohibited information, the privacy service will block or allow API calls. In some cases, it can also manipulate the results of API calls to ensure compliance with privacy policies.  
For example, there is an application that allows access to the accelerometer, magnet, gyroscope, and GPS. As mentioned in Section 3.3, the app can use pedestrian dead reckoning to infer positions with higher accuracy. Our approach detects resource usage from the application, predicts that the higher accuracy location can be revealed, and executes the corresponding policy enforcement.  
Colluding attacks are also easily detected by our mechanism as we monitor which information becomes available to each application, as far as the information exchange between malicious applications is over standard mechanisms, such as intents or binder. However, if those applications use the covert channel that cannot be detected by monitoring the API calls, they cannot be detected. The covert channel-based attacks are difficult to prevent and there are proposals in the literature.  
Since our system design is applied to Android systems, for the API monitoring and the data flow detection that we need for this work, we refer to API monitoring using \"Cydia Substrate\" [38], \"Xposed\" [39], \"TaintDroid\" [27], and our previous work [29].
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 5 Conclusions  
In this paper, we propose a new uplink synchronization method for NB-IoT by calculating the terminal's TA value and achieving uplink synchronization through detection of the NPSS broadcasted by satellites. To address the significant Doppler frequency shifts present in LEO satellites, an MSA algorithm is designed to enhance NPSS detection performance by exploiting the symmetry of NPSSs. An additional 33% increase in computational complexity results in improved NPSS detection performance. TDOA equations are established for different visible satellite scenarios, and uplink synchronization is achieved by combining terminal self-positioning with satellite ephemeris. Finally, compared to traditional GNSS-assisted approaches, the method proposed in this paper lowers terminal power  
Figure 19: Power consumption comparison between proposed method and GNSS-based method.  
Figure 18: Impact of different T values on TA estimation.  
consumption by 15-40% and maintains an uplink synchronization success rate exceeding 98%, even in negative SNR environments.  
At present, our work remains at the simulation level. In the future, we plan to investigate its feasibility at the hardware level.  
Methodology, Q.Q. and T.H.; Investigation, Q.Q., T.H. and G.Z.; Writing--original draft, Q.Q.; Writing--review & editing, T.H. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [No.U21A20450, No.62171234, No.61971440].  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Finite-Element Setup
### Geometry and 2D-to-3D Extrusion  
A consistent 2D cross-section, containing five distinct segmented bundles as shown in Figure 1, is imported into ANSYS Mechanical APDL as an IGES file. Each segment uses hexagonally packed sub-strands. The cross-section area for each segment is identical across all lay-length models; only the helix pitch \(\lambda\) changes how the segment is swept along \(z\).
## 5 Results
### Evaluation of Overfitting Risk During LoRA Fine-Tuning  
This experiment aims to evaluate whether the proposed GLR framework exhibits signs of overfitting during fine-tuning, with particular focus on the performance gap between the validation and test sets. As shown in Table 6, GLR achieves strong performance on the test sets across all three datasets, with MRR scores of 0.804 on UMLS, 0.507 on FB15K-237, and 0.679 on WN18 RR. In comparison, slightly lower performance is observed on the corresponding validation sets, where MRR scores are 0.781 on UMLS, 0.479 on FB15K-237, and 0.653 on WN18 RR. These results suggest that a mild degree of overfitting occurs during training. Nevertheless, its impact on final reasoning performance remains limited.  
In addition, prior studies have suggested that incorporating dropout within the LoRA pathway can effectively mitigate overfitting during fine-tuning [52]. Although the current study does not adopt such a mechanism, future work may consider integrating dropout regularization into the Graph-CoT-based LoRA fine-tuning process to further enhance the generalization capability of the GLR framework.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Results  
Table 3 shows the models investigated for zero-shot classification, while Table 4 shows the OA zero-shot accuracy produced by the different CLIP/Open-CLIP models on the three datasets. For Merced, the top three performing models for the six different prompt templates are xlm-roberta-large, ViT-H-14 from Open-CLIP, and ViT-H-14 from CLIP, with average accuracies of 77.00%, 72.00%, and 71.33%, respectively. For AID, xlm-roberta-large followed by RN50x64 from CLIP and ViT-H-14 from Open-CLIP exhibit the best behavior, yielding average accuracies of 68.33%, 62.33%, and 61.17%, respectively. For NWPU, ViT-H-14 from Open-CLIP yields the highest average accuracy with 66.83%, followed by convnext_large_d as well as from Open-CLIP and ViT-L-14 from CLIP with accuracies of 63.17% and 62.83%, respectively. However, we observe that xlm-roberta-large also yields an accuracy of 62.50%, which is very close to ViT-L-14 from CLIP. By averaging the results across the three datasets, we notice that the top three performing models are xlm-roberta-large, ViT-H-14 from Open-CLIP, and ViT-L-14 from CLIP, confirming the generalization ability of models with large parameters compared with models with a moderate or low number of parameters. Figures 3-5 show the confusion matrix for used datasets obtained by xlm-roberta-large and ViT-H-14. \begin{table}
\begin{tabular}{c c c} \hline \hline  & (A) & \\ \hline Dataset & \#Classes & \#Images \\ \hline Merced & 21 & 2100 \\ \hline AID & 30 & 10,000 \\ \hline NWPU-RESISC45 & 45 & 31,500 \\ \hline  & (B) & \\ \hline Dataset & \#Classes & \\ \hline Merced & agricultural, airplane, baseballdiamond, beach, buildings, chaparral, densreesidential, forest, freeway, golf course, harbor, intersection, mediumresidential, mobilehomerpark, overpass, parkinglot, river, runway, sparseresedential, storageetanks, tenniscourt & \\ \hline AID & airport, bare land, baseball field, beach, bridge, center pivot, church, commercial area, dense residential, desert, farmland, forest, industrial area, meadow, medium residential, mountain, park, parking lot, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks, viaduct & \\ \hline NWPU-RESISC45 & airplane, airport, baseball diamond, basketball court, beach, bridge, chaparral, church, circular farmland, cloud, commercial area, dense residential, desert, forest, freeway, golf course, ground track field, harbor, industrial area, intersection, island, lake, meadow, medium residential, mobile home park, mountain, overpass, palace, parking lot, railway, railway station, rectangular farmland, river, roundabout, runway, sea ice, ship, snowberg, sparse residential, stadium, storage tank, tenniscourt, terrace, thermal power station, and wetland. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RS datasets used in the experiments: (A) general statistics and (B) names of classes. _Appl. Sci._**2023**, _13_, 12462
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 2 Related Work
### Noise Disturbance  
Background noise is a main factor affecting the accuracy of object detection, especially in remote sensing images with complex backgrounds, where large amounts of noise can easily down small objects. The attention mechanism is an effective method to detect objects from noise interference. For example, [PERSON] et al. [21] used an attention mechanism in each feature map to enhance the feature and reduce the influence of background noises. [PERSON] et al. [17] designed a spatial- and scale-aware attention module, which is helpful for detecting objects with sparse texture and low background contrast. [PERSON] et al. [18]suppressed background noise by adding a supervised multi-dimensional attention network. [PERSON] et al. [22] designed a saliency pyramid, which combined a saliency algorithm with a feature pyramid network to weaken the influence brought by background noises. The saliency pyramid was combined with a global attention module branch to further enhance the semantic information and the expression ability of object features.  
Attention mechanisms can selectively emphasize informative features to improve detection performance. Among them, channel attention and position attention are two important mechanisms. Channel attention, such as SENet [23], computes channel attention weight with the help of global pooling and provides notable performance gains. However, channel attention only considers inter-channel information, ignoring the importance of positional information, which is the key to capturing object structure. CBAM [24] attempts to exploit positional information by reducing the channel dimension of the input tensor, and then uses convolution to compute spatial attention. However, convolution can only capture local relations and is unable to retain more context information that is essential for vision tasks [25]. How to choose the suitable attention mechanism, or even build a hybrid attention, which is beneficial for small object detection and background noise suppression, is a problem worthy of in-depth study.
# Assessment of Pesticide Contamination of Groundwater from Titu-Sarata Plain, Romania
###### Abstract  
In Romania, groundwater is an important source of drinking water, especially in rural areas. This study investigated the concentrations of organophosphorus, carbamate, and triazine pesticides (OPs) along with organochlorine pesticides (OCPs) in groundwater samples collected from the Titu-Sarata Plain. Sensitive analytical techniques were employed, including Ultrahigh-Performance Liquid Chromatography coupled with Q Exactive(tm) HF Hybrid Quadrupole-Orbitrap(tm) Mass Spectrometry (UHPLC-Orbitrap-MS/MS) and Gas Chromatography coupled with an electron capture detector (GC-ECD). Environmental and human health risks were assessed in the case of pesticides that exceeded the maximum allowed concentration. The environmental risk assessment (ERA) revealed significant risks associated with Phosdrin, Phorate, and pp()DDE. Additionally, particular concerns arose from the presence of Aldrin and Dieldrin, which pose a high carcinogenic risk, especially through groundwater consumption in agricultural areas. The results of this research highlight the need for the implementation of a continuous quality monitoring program for groundwater in the agricultural regions that were studied.  
pesticides; groundwaters; environmental risk; human health risk Article
# Language Bias in the Google Scholar Ranking Algorithm
## 2 Prior Studies  
Google Scholar has been the subject of numerous studies, aimed, above all, at assessing its quality [44; 45; 46; 47; 48; 49; 50; 51; 52; 53] and search effectiveness [45; 54], determining whether it is a suitable tool for conducting bibliometric studies [55; 56; 57; 58; 59; 60; 61] and evaluating author impact using the h-index [62; 63; 64; 65; 66; 67].  
In contrast, few studies have specifically examined how the Google Scholar ranking algorithm works [19; 20; 21; 22]. One of the reasons for this is probably the erroneous belief that Google Scholar uses the same or a very similar algorithm to the one used by the general Google search engine. However, the few studies of ranking by relevance in Google Scholar that have been conducted [19; 20; 21; 22; 23; 24; 25] conclude that it is a specific algorithm, adapted to the particular characteristics of scholarly documents; nevertheless, some ranking factors may be the same in both cases, such as the weight attached to keywords in the title [19]. Other factors, though, are clearly specific, such as the number of citations received, which is unique to Google Scholar [21; 25].  
Moreover, the frequency of keywords in the text of the article does not appear to be a factor that the Google Scholar algorithm takes into account [19]. Yet, there is evidence that the date of publication is of relevance, given that according to [PERSON] and [PERSON] [19] older articles have a higher ranking than more recent publications.  
A further factor identified by various authors is that the search term has to coincide exactly with a term in the documents retrieved and, moreover, that Google Scholar does not expand its searches through synonyms of keywords in the same way that Google does [68; 69].  
Where there is greater consensus is in attributing considerable weight to the number of citations received in Google Scholar's calculation of ranking by relevance [19; 23; 24; 25]. Thisis hardly surprising, given that Google Scholar is a bibliographic database and traditionally tools of this type use citations received as a means for sorting their results page.
## 3 Proposed Framework
### Semantic Segmentation  
Semantic segmentation provides object and stuff class labels for every pixel in a presented image. This process is also known as a high-density labeling task [88]. Images include widely diverse explicit and implicit information and meanings. Moreover, semantic segmentation provides a comprehensive scene description that includes information related to object category, location, shape, and context [89]. Semantic segmentation has numerous applications such as medical image processing [90], self-driving cars [91], structural damage mapping [92], vehicle detection [93], remote sensing [94], monitoring photovoltaic solar plants [95], power line cable inspection [96], building extraction [97], drone detection [98], classifying cooking activities [99], socially assistive robots [100], and industrial automation [101]. Representative semantic segmentation models include SegNet [102], U-Net [72], DeepLabV3+ [103], HRNet [104], SegFormer [41], Segmenter [42], and ConvNeXt [105].  
For visual tasks, vision transformer (ViT) [106] models perform better than ConvNets [4] if segmentation targets are objects [107]. By contrast, for stuff classes, especially in textures, the convolution mechanism provided by ConvNets demonstrated superiority, including in feature representations based on multilayered relations, compared to attention mechanisms provided by ViT [108]. [PERSON] et al. [109] indicated that the weaker ViT inductive bias is generally found to cause an increased reliance on model regularization or data augmentation when training on smaller training subsets. Therefore, for extracting sufficient ViT performance, very large datasets such as ImageNet-21K [110] or JFT-300M [111] are necessary for pre-training [112].  
Considering a balance between classification accuracy, implementation platform, and processing efficiency, DeepLabV3+ [103] was used for this study. This network flexibly extracts features with parallel processing of multiple filters that contain arbitrary resolutions by spatial pyramid pooling. The automatic formation of a decoding structure that propagates spatial information held by an encoder provides extraction of clear boundaries between objects. DeepLabV3+ [103] was pre-trained using the COCO-Stuff [113] open benchmark dataset, which provided comprehensive annotations of 172 classes. Based on a comparison of results obtained from our earlier studies [114; 115], Xception-65 [116] was used for the network backbone in this study. Here, the semantic segmentation module can be switched flexibly to state-of-the-art or practical backbones according to technological progress, target problems, and specifications, such as ConvNeXt [105], Segmenter [42], SegFormer [41], TransFG [117], MetaFormer [118], and K-Net [119].
## 5 Discussion and Implications for X-ray/Gamma-ray Production by Subsequent-Stroke Leaders  
As noted in the Introduction, subsequent leaders discussed in this paper traverse warm (reduced air density) channels. For such channels, the critical runaway breakdown field is considerably (about an order of magnitude) lower than in cold air. We calculated the electric fields expected to exist at the tip of a subsequent leader, which were used for computing the Coulomb force, which, in turn, was compared to the friction force for warm air, as per the 3000 K friction curve shown in Figure 1. We also examined the dependence of electric field peak on the leader model input parameters, including the prospective return-stroke peak current (a proxy for the leader tip potential) and leader propagation speed.  
Based on the sensitivity analysis presented in Section 4.2 above, we found the \"worst case\" combination of model input parameters, such that the electric field peak is maximized: \(z_{p}=\)150 m, \(I_{p}=\)55 kA, \(H=7.5\) km, and \(v=0.5\times 10^{7}\) m/s. For these values, the electric field peak predicted by our model is 20 MV/m (see Figure 10). The Coulomb force acting on an electron corresponding to a field of 20 MV/m is considerably larger than the peak of the friction curve for warm air (~4 MV/m; see red curve in Figure 1) allowing all electrons (regardless of their energy) to run away. This means that a relativistic runaway process does not require the presence of a super-energetic cosmic-ray particle and can start from the ambient electron distribution. The event presented in Figure 10 can be viewed as representing the very intense X-ray/gamma-ray producer reported by [PERSON] et al. (2012) ([2], Figure 7, bottom panel). That stroke followed the previously formed (warm) channel to ground and had the NLDN-reported peak current of 55 kA and the leader speed, inferred from the measured leader duration and assumed total channel length of 7.5 km, of \(8.3\times 10^{6}\) m/s, values similar to those used in computing the E-field waveform shown in Figure 10.  
The present study provides additional support to the elevated-temperature scenario (previously considered by [PERSON] et al. (2012) [2] and [PERSON] et al. (2015, 2019) [3; 17]), which requires realistic (confirmed by modeling) electric fields and no energetic electrons from external sources. Furthermore, we have shown that subsequent-leader electric fields can be high enough to overcome the warm-air friction curve, but not strong enough to overcome the cold-air friction curve, the latter one being applicable to first-stroke leaders. This implies that in, say, a two-stroke flash with both strokes having the same intensity, the second stroke may be producing X-rays/gamma-rays, while the first stroke may not.
# Optimizing Boleaching for Printed Circuit Board Copper Recovery: An AI-Driven RGB-Based Approach
## 2 Materials and Methods
### Experimental System Setup  
The bioleaching process was divided into four key operational stages to maintain consistency and control over experimental conditions:  
Material Preparation: Shredded copper cables were chosen as the starting material and processed in a biologically active solution within a bioreactor. The solution was enriched with Fe (III) ions produced by iron-oxidizing bacteria, establishing a controlled environment for copper leaching.  
Bioleaching Process: Operational parameters in the bioreactor were maintained within optimal ranges for microbial activity, specifically at 31 \({}^{\circ}\)C and a pH range of 1.6 to 1.8. These conditions facilitated efficient copper ion dissolution from the solid matrix into the solution, enhancing microbial effectiveness.  
Sensor Network Monitoring: An array of sensors continuously measured critical parameters such as pH, temperature, and metal ion concentration during the bioleaching process. RGB sensors tracked color changes in the solution, which were linked to Fe (III)and Cu (II) concentrations, offering a non-invasive method to monitor metal dissolution, as explained in Section 1.  
Data Transmission and Logging: Sensor data were transmitted in real-time to a central server via the MQTT protocol, ensuring low-latency and reliable data transfer. This setup enabled continuous data acquisition over multiple bioleaching cycles (72-96 h) and interval-based sample analyses, generating a comprehensive dataset for model training that captured both standard and edge-case operational conditions.
# Secure Cooperative Routing in Wireless Sensor Networks
## 2 Materials and Methods
### Simulation Parameters  
Simulations for the proposed MIDS are simulated using MATLAB R2023b. A WSN is built through deploying \"200\" sensor nodes in the \"100 \(\times\) 100 m\({}^{2}\)\" simulation area. BS is placed at the center of the WSN which is reflected as the optimal placement for the efficient use of energy. Out of \"200\" sensor nodes, \"4\" nodes are positioned as anchor nodes for effective localization, and the rest \"196\" are non-anchor sensor nodes. Initially, the network has the energy of \"1 J\". Some parameters that are used during the simulation process of the proposed MIDS algorithm with their values are shown in Table 1 and Figure 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulation Area & 100 \(\times\) 100 m\({}^{2}\) \\ Sensor Nodes & 200 \\ Initial Energy (I\({}_{e}\)) & 1 J \\ Simulation Time & 306.0459 s \\ Termination Threshold & \\ (ET\({}_{\rm TH}\) = 0.02) & mean (Ie) \(<\) N \(\times\) ETH J \\ Termination Threshold & \\ (For First Node Die) & 0.01 J \\ \(\varepsilon_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(\varepsilon_{amp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ Length of Data & 5000 bits \\ \(\mathrm{e}_{r}\), \(\mathrm{e}_{\mathrm{t}}\) & 50 nJ \\ \(\mathrm{d}_{\mathrm{o}}\) & 87.7 m \\ Anchor Nodes & 4 \\ Non-anchor Nodes & 196 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and values.