esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 1 Introduction
In parallel, satellite networks are evolving, and new advanced services are provided through both GSO (Geostationary Satellite Orbit) and especially NGSO (Non-Geostationary Satellite Orbit) in low orbit (e.g., VLEO, LEO and MEO) systems using different frequency bands. Direct-to-cell (D2C) communications are meant to provide satellite-based connectivity to mass-market terrestrial mobile handsets with the aim of providing additional coverage mainly in unserved rural areas. The implementation of D2C services is expected to use MSS (Mobile Satellite Service) frequency bands. Moreover, the WRC-27 agenda item 1.13 [6] foresees the study of possible spectrum allocation to the MSS for direct connectivity between space stations and IMT user equipment in the IMT frequency range. The European Commission has requested the Radio Spectrum Policy Group (RSPG) to form an opinion on the EU-level policy approach for the use of satellite direct-to-device connectivity. In the framework where new 6G services are expected to be developed, no single frequency range meets all of the spectrum requirements needed to implement 6G services. The RSPG has recently launched a public consultation on 6G strategic vision [8], aiming to identify the spectrum needs to facilitate the initial launch and operation of 6G networks/services from 2030. The RSPG intends to launch a 6G spectrum roadmap during 2026-2027 in order to identify which frequency band(s) should be made available for the 6G mass market. In order to improve spectral efficiency, the spectrum for the new 6G systems is expected to be shared between various services and users. It is therefore very important to plan actions that promote the shared use of the spectrum, both between terrestrial networks (TN) and between terrestrial and non-terrestrial networks (TN-NTN). The technical analysis presented in this work represents the continuation of coexistence studies between terrestrial and non-terrestrial systems in the 27.5-29.5 GHz band carried out in [9]. In [9], results of a coexistence analysis between fixed-satellite-service Earth-to-space (FSS E-S) interferer and fixed-service (FS) Wireless Local Loop Point-to Point (FSWLL P-P) victim systems have been presented. In this paper, we extend the coexistence analysis to evaluate the coexistence condition between FSS E-S and FS WLL Point-to-Multipoint systems. The adopted methodology and the general conclusions derived regarding spectrum sharing conditions can be easily extended to other coexistence analyses between different services.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Security of Grid Edge Management System  
Network-constrained energy management systems at the grid edge depend on the interconnection of a circuit segment to deliver and receive load measurement. However, this raises the risk of invisible false data injection attacks (FDIAs). To eliminate this risk, the paper [118] proposes a framework to detect the FDIAs on loads and replace the anomalous data with forecasted values. The detection algorithm in this framework comprises seven steps. The first step is to implement state estimation with the received measurements. Then, the bad data detection algorithm is proposed to check for anomalies in the load. If anomalous data are detected, the system will replace the bad data with the forecasted values. Otherwise, a data-driven autoencoder (AE) approach in another layer is responsible for identifying potential attacks in the measurements. If there is no indication that the measurements are attacked, the measured data can be used in a grid-edge energy management system. This security enhancement framework is tested by using an IEEE 13-bus system with three integrated DERs. The evaluation results show that the proposed framework can effectively mitigate the risk of FDIAs and enhance the resilient operation of grid-edge energy management systems. The emergence of inverter-based DERs prompts diverse renewable energy sources to integrate into the primary grid. Inverter-based DERs bring many considerable benefits, such as improving power quality by minimizing harmonics and responding rapidly to fault isolation. Although inverter-based DERs can provide improvements, there are still challenges that need to be resolved. The first challenge, as presented by [PERSON] et al. [119], is that the inherent instability of renewable energy sources threatens the reliability of the system. The existing forecasting method still cannot predict the variance between estimated and real values, which can result in serious reliability issues. The second challenge is the stability of the system. Low inertia can reduce the system's stability margin. To address the limitations mentioned above, the authorsproposed a cyber-physical security assessment framework. The framework consists of a cyber-physical system model involving dynamic attributes of inverter-based DERs. The proposed framework was evaluated with IEEE 13-node and 123-node test feeders, and the results show that this framework demonstrates significant improvement in resilience and stabilization.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 3 Method
### The Hybrid Approach with LSB
```
1. function hybridImageEncryption(original image, secretMessage): encryptedMessage = hybridEncryptionAlgorithm(encrypt(secretMessage))  stegolmage = embedMessageLSB(originalImage, encryptedMessage)  return stegolmage
2. function hybridImageDecryption(stegolmage): extractedMessage = extractMessageLSB(stegolmage)  decryptedMessage = hybridDecryptionAlgorithm(decrypt(extractedMessage))  return decryptedMessage
3. function embedMessageLSB(originalImage, message): stegolmage = copy(originalImage)  binaryMessage = convertToBinary(message) bitIndex = 0 for each pixel in originalImage: if bitIndex >= length(binaryMessage): break  pixelLSB = getLeastSignificantBit(pixel)  modifiedPixel = setLeastSignificantBit(pixel, binaryMessage[bitIndex])  stegolmage.setPixel(modifiedPixel)  bitIndex = bitIndex + 1 return stegolmage
4. function extractMessageLSB(stegolmage): extractedMessage = \"\" for each pixel in stegolmage: pixelLSB = getLeastSignificantBit(pixel)  extractedMessage = extractedMessage + pixelLSB  return extractedMessage
5. function hybridEncryptionAlgorithm(plaintext): symmetricKey = generateSymmetricKey() encryptedSymmetricKey = asymmetricEncryptionAlgorithm(symmetricKey) ciphertext = AES.encrypt(plaintext, symmetricKey) return encryptedSymmetricKey + ciphertext
6. function hybridDecryptionAlgorithm(ciphertext): encryptedSymmetricKey = extractEncryptedSymmetricKey(ciphertext) symmetricKey = asymmetricDecryptionAlgorithm(encryptedSymmetricKey) encryptedMessage = extractEncryptedMessage(ciphertext) decryptedMessage = AES.decrypt(encryptedMessage, symmetricKey) return decryptedMessage
7. function encrypt(message):
8. Perform encryption on the message using a specific Hybrid encryption algorithm
9. function decrypt(ciphertext):
```  
**Algorithm 1** Hybrid approach with LSB encryption.
## 2 System Architecture
### Wearables  
The system architecture, as shown in Figure 2, is compatible with any wearable device that is provided with a software development kit (SDK) to enable access to raw or minimally  
Figure 2: Architecture of the system for the collection and end-to-end data processing from wearable sensors.  
processed signals collected by the sensors, either directly or through a smartphone or tablet paired with the device. To date, we have deployed the system with a family of smartwatches and wristbands from Garmin Ltd. (including the Garmin Fenix 6) and with the Samsung Galaxy Watch 3. The wearables integrate two PPG sensors for monitoring HR and SpO\({}_{2}\), and a three-axis accelerometer for detecting movement in the \(x\), \(y\), and \(z\) directions. The watch firmware uses accelerometer signals to calculate step counts that are posted every 1 min.
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 11 Targeted Gene Regulation via Engineered Exosomes
For example, the exosomes isolated from the tumor cells SKOV3 were used as the CRISPR/Cas9 delivery platform, which was loaded by electroporation to inhibit the cancer cell proliferation by activating the apoptotic pathway. More specifically, the exosomes loaded with CRISPR/Cas9 could inhibit polymerase-1 (PARP-1) expression and induce apoptosis in ovarian cancer cells. In addition, the inhibition of PARP-1 by CRISPR/Cas9, mediated by genome editing, increased the cell sensitivity to cisplatin (an oncocytotoxic drug) [123]. In another study, the exosomes from the HEK 293T cells, previously transfected with the plasmids containing sgRNA and spCAS9, and the exosomes with the fused exosomal membrane protein CD63 with GFP, which can bind to the GFP antibody fused with Cas9 protein, were isolated. The A549 cells were then treated with exosomes, and the efficiency of their delivery was assessed by fluorescent signals (the reporter gene-GFP), thereby confirming that the exosomes were taken up by the recipient cells. This loading of the CRISPR-Cas9 components contributed to efficient genome editing in the reporter cells [124]. Although the data on the plant exosomal vesicles are still absent, the opportunity of their use in the similar research should also be realized. Figure 4: The use of exosomal vesicles for genetic material delivery. 1—exosome loading with therapeutic macromolecules; 2—exosome delivery; 3—genetic material release upon fusion of the exosome with the cell plasma membrane; 4—miRNA—mediated regulation of gene expression through activation of the RNA-induced silencing complex (RISC); 5—nuclear import of Cas9—ribonucleoprotein (RNP) and plasmid DNA; 6—mRNA nuclear export for translation. Exosome-mediated methods are advantageous compared to the alternative methods for genetic material delivery (mainly liposomes and viral vectors) for several reasons. The first factor is that exosomes have extremely low immunogenicity, which increases the effectiveness of therapy and, most importantly, makes repetition possible [125]. Secondly, exosomes are able to penetrate the blood-brain barrier, as well as other biological barriers, opening up the possibility of targeting hard-to-reach disease cases [126]. Thirdly, the half-life of exosomes in the blood is longer than that of artificial liposomes, which is achieved due to the presence of various transmembrane and membrane-bound proteins on the exosome surface, allowing them to avoid phagocytic clearance [127]. The effective targeting by exome-based vectors may be possible in the future by engineering the composition of specific proteins on their surfaces. For the purposes of genome editing technology, exosomes can be loaded not only with a plasmid containing the CRISPR/Cas9 system, but also in a more advanced way, namely Cas9 ribonucleoprotein (RNP), which allows avoidance of the undesirable consequences of bacterial gene expression in patient cells [124]. All of these advantages will allow the creation of more effective and safer drugs for patients.
## 4 Experiment
### The Performance of Tracking-by-Detection Algorithm Compared with Other Algorithms  
In pedestrian flow statistics algorithms, the performance of detecting and tracking algorithms has an important impact on the counting results. Therefore, we evaluate the proposed algorithm by comparing the comprehensive performance of the proposed detecting and tracking algorithm with other algorithms. Here we choose to use the 2 DMOT2015 benchmarks [38] to evaluate the performance of the algorithm quantitatively. 2 DMOT2015 benchmark is a well-known framework for the fair evaluation of multi-pedestrian tracking algorithms. The dataset includes 22 image sequences, of which 11 are training sets and 11 are test sets. Image sequences come from several influential pedestrian detection and tracking datasets, including KITTI, ADL, ETH, PETS and TUD. The main evaluation indicators are as in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequence** & **Count Result** & **GT** & **FP** & **FN** & **TP** & **Precision** & **Recall** & **F1-Score** \\ \hline Seq1 & 552 & 567 & 21 & 36 & 531 & 96.20\% & 93.65\% & 94.91\% \\ Seq2 & 411 & 412 & 18 & 19 & 393 & 95.62\% & 95.39\% & 95.50\% \\ Seq3 & 571 & 572 & 21 & 22 & 550 & 96.32\% & 96.15\% & 96.24\% \\ Seq4 & 923 & 955 & 18 & 50 & 905 & 98.05\% & 94.76\% & 96.38\% \\ Seq5 & 669 & 693 & 16 & 40 & 653 & 97.61\% & 94.23\% & 95.89\% \\ \hline TOTAL & 3126 & 3199 & 94 & 167 & 3032 & 96.99\% & 94.78\% & 95.87\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The evaluation results of the algorithm in test videos. Figure 7: The actual operation effect of the algorithm. Among them, MOTA is a comprehensive evaluation of FP, FN and IDSW. Its formula is as follows:  
\[\text{MOTA}=1-\frac{\sum_{t}(\text{FN}_{t}+\text{FP}_{t}+\text{ IDSW}_{t})}{\sum_{t}GT_{t}} \tag{4}\]  
where \(GT_{t}\) is the number of ground truth in frame \(t\). MOTP focuses on the average difference between TP and its corresponding ground truth. The formula is as follows:  
\[\text{MOTP}=\frac{\sum_{t,i}d_{t,i}}{\sum_{t}c_{t}} \tag{5}\]  
Where \(d_{t,i}\) denotes the overlap of the bounding box \(i\) with its corresponding ground truth, \(c_{t}\) denotes the amount of bounding boxes which match ground truths successfully. To verify the comprehensive performance of detector and tracker, we compare the proposed algorithm with other algorithms using private detector, including MDP_Subcnn [39], DMT [40] and Sort [41]. The specific comparison results are shown in Table 4.
Sensors_**2010**, _10_, 5425-5442; doi:10.3390/s100605425  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 College of Computer and Information, Hohai University, Nanjing, China;  
E-Mails: [EMAIL_ADDRESS] (L.Z.X.); [EMAIL_ADDRESS] (H.B.W.); [EMAIL_ADDRESS] (J.S.)  
2 School of Engineering, University of Guelph, Guelph, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-25-83787793; Fax: +86-25-83787793.  
_Received: 9 April 2010; in revised form: 29 April 2010 / Accepted: 20 May 2010 / Published: 1 June 2010_  
**Abstract:** The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks.  
**Keywords:** environmental monitoring; meteorological and hydrological telemetry; Wireless Sensor Networks; Differential Evolution Algorithm; LEACH protocol
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### Methodology  
With regard to the methodology used, it can be described as follows:  
1. **Selection and analysis of different datasets**. Specific Pol databases are still sparse, in particular considering the parameters proposed for the Pol definition, e.g., speed, visit time, time granularity and geo-location. The dataset selection and cleanup are presented in Section 5.2
2. **Dataset cleanup and validation**. Selected datasets have been transformed into new datasets also integrating time travelled across sequential waypoints of a trajectory; distance travelled; speed; day of the week; user id (obfuscated); means of transport.
3. **Selection of ML algorithms**. The algorithms proposed in this work, DBSCAN and LOF, were applied to detect outliers with the help of Python programming language, proceeding where necessary to any adjustments of parameters or possible tuning of the algorithms.
4. **Performance evaluation**. The accuracy of the algorithms used was measured, verifying their validity and applicability throughout the study, and verifying which factors were preponderant for the analysis.
## 2 Related Work
### Linguistic Features for Writing Quality  
Scholars have attached much importance to linguistic features and their interactions with writing quality [53; 54; 55; 56; 57; 58]. Generally, linguistic features characterizing writing quality can be categorized into three aspects: vocabulary, syntax, and cohesion [59; 60]. In terms of lexical level, scholars have demonstrated that first language (L1) writers tend to use sophisticated phrasal constructions, greater lexical variation, less common words and bi-grams, more imageable words, and academic vocabulary [60; 61; 62; 63; 64; 65; 66; 25; 67]. Similar results have been observed in second language (L2) writing, revealing that high-quality essays are characterized by longer texts, a broader and more sophisticated vocabulary, and the use of less common and familiar words [68; 69; 70; 71; 72; 73; 74].  
From a syntactic-level perspective, scholars have indicated that syntactic sophistication is positively related to the writing quality of L1 writers [59; 60; 75; 76]. Subsequent studies have revealed a positive correlation with larger noun phrases, more modifiers, and additional words before the main verb and a negative correlation with simple declarative sentences and essay quality [25; 77]. However, not all studies have reported a significant relationship between syntactic-level features and writing quality [25; 78]. For L2 writers, complex nominal per clause has been identified as an indicator of writing quality. Additionally, the use of clausal subordination [56; 72; 79] and the frequency of passive voice are also positively correlated with writing quality.  
Regarding cohesion, high-quality essays written by L1 writers are characterized by fewer positive connectives, less content word overlap, and increased usage of hedges and contrast expressions [77; 80; 81; 82]. Research on L2 writers, however, yields mixed outcomes: cohesive devices such as semantic and lexical overlap, connectives, and givenness are negatively correlated with writing quality [55; 68].  
While existing research has considerably advanced our understanding of how linguistic features correlate with overall writing quality, there has been limited investigation of the correlation between linguistic features and different constructs of writing quality. Exploring multi-dimensional rating traits offers insights into linguistic features that enhance writing instruction across different writing constructs and covers a coherent picture of qualified linguistic features for model construction per rating trait. Furthermore, the advancement of computational analytic tools now allows extracting a wide array of fine-grained linguistic features, further enriching multi-dimensional writing assessment and model development.
# AHC: Advanced Network-Hiding Access Control Framework
## 4 Methods  
### Architecture  
The scalable architecture designed in the AHAC framework consists of multiple key components, including Agent, Network-Hiding Server (Server), Access Control (AC) system, Authorization Service Provider (ASP), and Protected Resources, as shown in Figure 1. These components communicate user authorization information, the true location of protected resources, and release policies through precise and efficient interactions, thereby achieving secure hiding and access control of servers where data resources are located in the network environment.  
The functions of each component are described as follows.  
AHAC Agent (Agent): This component is responsible for initiating the request. It can take various forms, including an SDK, process, client application, mobile app, browser extension, or even a server backend service and other independent programs.  
Network-Hiding Server (Server): This component is specifically responsible for processing and verifying knock requests. It typically exists as a server program. The server not only verifies knock requests but can also interact with external authorization service providers to perform authentication operations and ensure the security and legitimacy of requests. Additionally, it controls network invisibility and access control to allow precise control and management of the access control system.  
Authorization Service Provider (ASP): In this extensible architecture, the ASP is a service interface provided by the resource owner to implement authentication and authorization of identities, devices, and policies, and to provide the real access address of protected resources. The services provided by authorization service providers typically include identity authentication services, device verification services, and policy permission verification services. The communication interface between the Server and the  
Figure 1: AHAC architecture diagram.  
network-hiding authorization service provider can use an HTTPS API or a customized TCP communication protocol to ensure the synchronization of verification interactions.  
Access Control (AC): The AC is the execution component of access control, which is usually implemented as a server program. The AC enforces a default \"deny all\" security policy and ensures the network invisibility of protected resources. It is responsible for granting access rights (such as IP address, port number, etc.) to authorized Agents, revoking access rights from Agents that have lost authorization, and executing release actions for Agents based on parameters returned by the Server.  
Protected Resource: This refers to valuable assets that resource providers are committed to closely guarding. These resources can exist in various forms, including but not limited to data API interfaces, data application servers, gateways, routers, and load balancers for cluster services.