esa-sceva/satcom-chunk-collection huggingface dataset:
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 2 Preliminaries
### Fingerprint-Based Positioning  
The first step in fingerprint-based positioning is the construction of a reference map, where a specific RP serves as the origin of the environment. At each predetermined coordinate, known as an RP, the RSSI values from various APs are recorded. Notably, the exact locations of the APs do not need to be known, as we assume the APs remain stationary, and their RSSI values serve as unique fingerprints for each RP. These RSSI measurements, taken multiple times at each fixed RP, form the input for positioning algorithms, with the corresponding RP locations as the output. The raw RSSI dataset for all RPs is defined as [29]  
\[S_{i}^{j}=m(L_{i},\epsilon_{i}^{j},\eta), \tag{1}\]  
where \(S_{i}^{j}\in\mathbb{R}^{R},i=1,\cdots,M,j=1,\cdots,N_{j}\) represents the \(R\)-dimensional RSSI measurements, \(L_{i}\equiv(\ell_{oi},\ell_{ai},\ell_{fi},\ell_{bi})\) denotes the location information of the mobile device, which includes Longitude, Latitude, Floor, and Building for the \(i\)th observation. Additionally, \(\epsilon\in\mathbb{R}^{d_{c}}\) captures the unobserved features, while \(\eta\in\mathbb{R}^{R}\) represents the error term in the model. The vector-valued function \(m\) characterizes the structural connection between the mobile device's observed and unobserved features and the associated \(R\) dimensional RSSI measurements.  
Two types of noise are present in the raw RSSI dataset shown in \(\epsilon\): small-scale fading and large-scale fading. Large-scale fading, which is position-dependent and nearly time-invariant, cannot be mitigated by simply averaging samples over time [16]. As a result, it persists in the dataset. Conversely, small-scale fading can be reduced by averaging the RSSI samples. The averaged RSSI values from the raw dataset in (1) can be represented as  
\[\tilde{S}=\begin{bmatrix}\tilde{S}_{1}\\ \vdots\\ \tilde{S}_{M}\end{bmatrix}_{M\times R} \tag{2}\]  
where \(\tilde{S}_{i}\) denotes the average RSSI value for all the AP in the \(i\)th RP. Many traditional positioning algorithms prefer to use this averaged dataset instead of the raw RSSI data, as it helps mitigate the effects of small-scale fading and reduces the computational time during the search process.  
After the fingerprint map is established, the user's current location is determined by comparing real-time data with the stored fingerprints. This process can be enhanced using machine learning algorithms such as WKNN or more advanced techniques like DNN. DNN improves accuracy by learning complex relationships between the RSSI and the user's location during the training phase.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 3 System Model
### Communication Slots  
The physical-layer RFID communication between the reader and the tags was based on slotted ALOHA. The reader starts a slotted frame, on the basis of which each tag randomly maps itself to one of the slots by a hash function. Thus, there are three categories of slots: (1) empty slot: selected by no tags; (2) singleton slot: selected by just one tag; (3) collision slot: selected by more than one tag. In this paper, singleton slots and collision slots are also called non-empty slots. By constructing the frame and handling the statuses of slots, the reader can accumulate the functional information of the expected tags for the practical application of RFID systems.  
To distinguish an empty slot from non-empty slots, the tag needs to transmit only 1 bit of information, which needs \(t_{s}\) time in a slot. Meanwhile, it takes \(t_{l}\) time in a slot to transmit 10 bits of information for distinguishing the three types of slots. The time for transmitting one tag ID (96 bits) from the tag to the reader is denoted by \(t_{tag}\). Generally, \(t_{tag}>t_{l}>t_{s}\).
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
#### 7.1.1 Node Distribution and Communication Range  
In traditional WSNs, sensor nodes are typically deployed in a random or clustered fashion (most likely giving them a mesh-like topology), facilitating short-range communication and proximity-based data aggregation. However, in thin LWSN monitoring applications, sensor nodes are spread out along a linear path, leading to longer communication distances and increased energy consumption for data transmission [155]. Techniques relying on close proximity for efficient communication, such as node clustering, become less effective in thin LWSNs due to the dispersed node distribution and extended communication range. This makes it challenging for existing energy-balancing schemes (e.g., cluster-based, multi-path, and energy-aware routing) designed for traditional WSNs to be directly applicable to LWSNs due to the unique characteristics of the nodes in LWSNs [154]. To achieve reliable data delivery in large-scale monitoring applications, traditional WSNs make use of multi-hop routing. However, to effectively utilize the multi-hop approach in LWSNs, it is advisable to reduce the distance of the network by deploying multiple sink nodes [156; 157; 158]. This approach helps to mitigate the energy hole problem [159; 160] and can also decrease the number of hops required for data transmission, thereby reducing the overall energy consumption. Also, a pure multi-hop approach to route data along the LWSN extending for hundreds or even thousands of kilometers can incur significant energy costs. As a result, data collection strategies employing mobile sinks [161], such as unmanned aerial vehicles (UAVs), are often more suitable compared to traditional WSN multi-hop routing approaches [41].  
Another major challenge in LWSNs is to ensure end-to-end packet delivery with a smaller number of relay nodes [27; 162]. This is because, unlike in traditional WSNs, nodes near the sink in LWSNs tend to deplete their energy more rapidly because they handle heavier traffic compared to other nodes in the network [21]. Over time, this uneven load distribution, commonly referred to as the \"relay burden problem\", leads to a disproportionate consumption of energy, leaving the nodes in close proximity to the sink node with significantly less energy [163]. Consequently, the risk of prematurely depleting the network's overall energy reserves and shortening its lifetime is greatly amplified. Moreover, these close-in nodes cannot afford extended sleep periods as they need to remain vigilant in idle listening mode to fulfill their relaying duties. Therefore, it becomes imperative to implement more intelligent methods like optimal node placement [99] andmobile sink [41] for distributing the traffic load across the network to ensure and prolong its operational lifetime.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Design Management
#### 3.1.2 Approaches to Data Auditing  
Data auditing in the context of cloud computing (CC) refers to the process of inspecting the services provided by the cloud service providers (CSPs), as well as the communication between different parties involved in the cloud environment [43]. The purpose of the auditing process is to ensure the integrity of transmitted data by employing various detection methods, including review, analysis, protocols, and observation [46]. Additionally, data auditing ensures compliance, thereby preventing any unauthorized breaches. There are two types of data auditing: external and internal audits. External audits are conducted by independent entities to evaluate and verify the integrity of data transmitted among participants in the cloud network. On the other hand, internal audits are performed by users to assess the data and services provided by the CSP, focusing on evaluating the privacy capabilities of the CSP [43]. Here, the authors have suggested organizing cloud-stored data using a multi-layered encoded structure. This involves breaking the data into smaller parts, each of which is encoded individually using various methods, e.g., base64 or other customized schemes. These encoded chunks are then tagged with metadata such as identifiers and verification codes (e.g., hash values) for improving the traceability and integrity-related checks.  
Retrieving the data includes collecting the relevant chunks using their identifiers, verifying the integrity through hash matching, and decoding each of the layers in reverse order to reconstruct the original data. The layered encoding structure improves privacy in several ways:  
_Concalment_: Encoded data appears meaningless without the decoding logic.  
_Multiple Barriers_: Even if one encoding layer is breached, others remain intact.  
_Built-in Verification_: Integrity checks ensure that any unauthorized modifications are detected.  
_Decoupled Storage_: Metadata and decoding information can be stored separately, reducing the risk of full data exposure.  
Further, the authors implemented an SQL (structured query language) procedure to support the organized storage and retrieval of these encoded components. The following is a simple example:  
--_SQL for inserting new encoded data_--  
INSERT INTO cloud_data (file_id, chunk_id, data_chunk, hash_code, uploaded_at)  
VALUES ('F001', 'C01', 'U2 FtcGxIRGF0 YQ==', 'd3486 ae9136e7856 bc42212385 ea797094475802', NOW0)):  
--_SQL for retrieving all chunks linked to a specific file_--  
SELECT chunk_id, data_chunk FROM cloud_data WHERE file_id = 'F001' ORDER BY chunk_id;  
--_SQL for validation of integrity_--  
SELECT chunk_id FROM cloud_data WHERE file_id = 'F001' AND hash_code!= SHA1(data_chunk);  
The above queries handle data entry, validation, and retrieval for ensuring efficient and secure cloud data management.
## 3 Results and Discussion
### Characterization of Starch/MCC Films
#### 3.2.6 Moisture Content and Water Activity Index  
The moisture content and water activity levels in packaging play a vital role in preserving the safety, stability, and quality of packaged products. By controlling these parameters, manufacturers can mitigate the risk of microbial contamination and chemical degradation, ensuring consumer safety and satisfaction.  
Figure 8: Water contact angle images droplets on the surface of the cassava starch/MCC films with times.  
Figure 7: Water contact angle of films (\(n=3\)).  
The moisture content of the starch film exhibited a slight decrease from 14.20% to 12.98% upon the incorporation of 5% (_w/w_) MCC (Table 5). [PERSON] et al. (2021) reported a similar trend in their study [47]. They found that the addition of 7% (_w/w_) MCC slightly decreased the moisture content of the cassava starch film from 14.24% to 13.55%. This reduction in moisture content can be attributed to the formation of more intermolecular hydrogen bonds between MCC and the hydroxyl groups of starch. Consequently, the availability of free -OH groups decreases, leading to a reduction in the interaction between water and the starch-OH groups. Consequently, the moisture content of the films tends to decrease [48].  
Water activity quantifies the amount of free moisture in a product and is defined as the ratio of the vapor pressure of water in a material to the vapor pressure of pure water at the same temperature [49]. A water activity index below 0.6 indicates that the environment is relatively low in available water, which can be inhibitory towards the growth of microorganisms such as _S. aureus_, _E. coli_, and other coliforms [50]. From the results, as shown in Table 5, all starch films have water activity values lower than 0.6, ranging from 0.48 to 0.54, indicating that molds, yeasts, and bacteria are unable to grow and proliferate, ensuring safety for use.  
The findings from all the results suggest that starch/3%MCC holds potential as a biocomposite film for medical packaging applications. It offers benefits such as excellent transparency, favorable mechanical properties, and surface hydrophobicity. Consequently, the starch/3%MCC film was chosen for further investigation in sterility determination and the impact of UV sterilization on its properties.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Concrete Strength  
Figure 17 shows the influence of the concrete strength on the pull-out behavior of notched perfobond shear connectors. When the concrete strength was increased from 30 MPa to 40 MPa, 50 MPa, 60 MPa, and 70 MPa, the pull-out resistance increased by 20%, 40%, 56%, and 55%, respectively.  
Figure 16: Influence of perfobond thickness. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 15: Influence of cut width. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
It was indicated that the increase of the concrete strength would lead to a great increase in the pull-out resistance of the notched perfobond shear connector. It was possibly because higher strength concrete resulted in a smaller damaged region of concrete dowels at the pull-out failure. Therefore, the pull-out resistance of the notched perfobond shear connector could be increased by using higher strength concrete. However, when the concrete strength was greater than 60 MPa, the pull-out resistance tended to stop increasing. The reason for this might be that higher strength rebar that matched the higher strength concrete was required to achieve full resistance of the concrete dowel in the hole.
## Data Analysis
### Questionnaire 3: Technology Acceptance Model  
The research model used at this stage of the study is the classical TAM shown in Figure 1[14; 16]. In the questionnaire, 13 questions about the perceived benefit for use of information technology, 13 questions about perceived ease of use, four questions about attitude change, seven questions about intention change, and two questions about measuring last behavior variable were asked. The materials in the measurement tool were derived from the work of [19]. In responding to the survey tool, the situations described by the materials are arranged as \"Totally Participating\", \"Partly Participating\", \"Participating\", \"Poorly Participating\", and \"Neither Participating\". The measurement tool was arranged according to the factors and the average of the responses taken from the students in response to the answers listed in the form, provided in Table 4.  
Besides, all students were informed in detail about the newly developed system before the surveys. Besides, demo sessions were held for 2 weeks for both groups to get used to the systems. In terms of perceived use and intention in Table 4, it is seen that the average is generally over four points. Four values correspond to the expression \"I agree\" on the scale. Accordingly, it can be said that the perceived usefulness and intentions of the participants to use information technology products are high. Figure 5: Week-assignment group note distribution. From the point of view of perceived ease of use, the highest arithmetic average (4.54) stands out as \"I can easily do what I want to do with my lesson thanks to the new laboratory system\". When we look at the same way from the standpoint of attitude, the highest arithmetic average (3.86) stands out as \"I am happy to use the new laboratory system\". Finally, the highest arithmetic mean (3.81) is the \"I use the new laboratory system very often outside the campus\" in the part of the behavior. Relationships between the TAM components were examined utilizing a correlation analysis and the results are listed in Table 5.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 6 Data Encoding and Decoding
### Data Packets  
We transmit data in the form of 56-bit frames, which consist of a frame header, payload, and frame tail. The frame header includes an 8-bit preamble, while the payload contains 40 bits. Finally, the frame tail comprises an 8-bit checksum as illustrated in Figure 23.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Interval (s)** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline
**bits** & 000 & 001 & 010 & 011 & 100 & 101 & 110 & 111 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Encoding table.  
Figure 21: Waveform during the modulation load of 2 PSK. (**a**) PSK signal modulation; (**b**) PSK time-domain signal.  
Figure 22: Pulse interval modulation.  
The frame header, transmitted at the beginning of each packet and comprising eight alternating symbols (\"10101010\"), facilitates the receiver in determining the channel signal's starting position. Moreover, it enables synchronization with transmission initiation and the calibration of other parameters, such as the current frame number and device power. The data frame represents the actual binary data to be transmitted for each data frame, with a payload size chosen as 40 digits. To detect and rectify transmission errors, an 8-bit RS error correction code is inserted at the end of the frame. The receiving end calculates this RS error correction code for the received payload, enabling the correction of any errors in data bits during transmission. An 8-bit RS error correction code can correct up to 4 bits of data at any given position [45].
## 5 Results
### Spectral Unmixing Results
#### 5.2.3 Sentinel-2 MSI  
The Sentinel-2 MSI results showed that _rhyodacites_ (Figure 5c) mainly appeared at the periphery of the study area in the form of scattered pixels of intermediate to high abundance. _Basaltic andesites_ (Figure 5f) generally appeared in low abundances, except from few sparse pixels, at Lakki plain, inside Stefanos crater, and in the periphery of the study area. The _alluvial deposits_ (Figure 5i) showed high abundances northeast of the study area around the Nis-1 and Nis-2 geothermal wells and inside Stefanos crater. The pattern of alluvial deposits resembled the corresponding alluvial deposits' pattern of Landsat-8/OLI image and, in a larger extent, compared with the ASTER corresponding abundance map. Concerning the _hydroxyl-bearing alteration_ (Figure 5l), the same distinctive spatial pattern was observed as in the ASTER and Landsat-8/OLI corresponding maps with the highest abundance values located at Lofos Dome, the central sector craters (Phlegethon, Mikros, and Megalos Polybotes craters), and inside Stefanos crater. Similar to the ASTER and Landsat-8/OLI corresponding results, the _sulfur_ abundance spatial distribution (Figure 5o) generally followed the pattern of the hydroxyl-bearing alteration.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 1 Introduction
Instead of considering the entire formation as a single rigid body, as in the virtual structure approach, the formation is treated as a mechanical linkage which is an assembly of rigid bodies connected by joints. As compared to a single rigid body, a mechanical linkage is able to change its geometric shape by coordinating the angles between each link. Figure 1 illustrates that, instead of remaining the same shape, different geometric shapes can be presented using a mechanical linkage. In other words, the formation shape can be reconfigured if the individual agents are controlled as particles in a mechanical linkage. The main advantage of the virtual linkage approach is that it can maintain the formation shape in high precision and is able to reconfigure the geometric shape when needed. On the one hand, if the angles between each link are kept at a constant value, the multi-link mechanism is expected to behave like a single rigid body and has high precision formation control. On the other hand, the formation shape can be reconfigured by varying the angle value between links. Another advantage is that there is no need to transmit all the agents' state information into a central location as in [19] due to the hierarchical architecture of the virtual linkage approach. The remainder of this paper is organized as follows. In Section 2, the definition of virtual linkage is presented. Section 3 illustrates the control strategy for the formation movement. Simulation results are in Section 4. Finally, in Section 5, some concluding remarks of this paper are given. Figure 1: Example of a single rigid body and a mechanical linkage.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
###### Abstract  
Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances landslide susceptibility prediction by integrating ensemble learning with systematic factor engineering. Four homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and two heterogeneous ensembles (bagging and stacking) were implemented to evaluate 14 influencing factors. The key results demonstrate the Digital Elevation Model (DEM) as the dominant factor, while the stacking ensemble achieved superior performance (AUC = 0.876), outperforming single models by 4.4%. Iterative factor elimination and hyperparameter tuning increased the high-susceptibility zones in the stacking predictions to 42.54% and enhanced XGBoost's low-susceptibility classification accuracy from 12.96% to 13.57%. The optimized models were used to generate a high-resolution landslide susceptibility map, identifying 23.8% of the northern and central regions as high-susceptibility areas, compared to only 9.3% as eastern and southern low-susceptibility zones. This methodology improved the prediction accuracy by 12-18% in comparison to a single model, providing actionable insights for landslide risk mitigation.  
landslide susceptibility; ensemble learning; factor engineering; random forest; XGBoost; LightGBM; CatBoost; Three Gorges Reservoir area +
Footnote †: journal: Applied sciences  
Article