esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 1 Introduction
Therefore, to better understand detection methods and their limitations, it is essential to understand how images can be manipulated in the first place [14]. There has yet to be a consensus on classifying all image manipulations; however, most papers recognize the existence of at least two types, copy-move, and splicing. In copy-move forgery, an area of an image is duplicated and pasted over another area of the same image. In splicing, however, an area of another image is pasted over the image. This manipulation can add new meaning to the image or conceal information; the resulting image is a fusion of two different images. Following the discussion on various approaches to identifying image manipulation, it is essential to highlight how this paper contributes to advancing the field. While existing studies often focus on datasets limited to specific tampering types or utilize algorithmically generated datasets (e.g., [15; 16; 17; 18]), this research employs a more comprehensive dataset. Notably, this dataset is manually curated and designed to encompass a broad spectrum of realistically manipulated images, covering all types of manipulation encountered in real-life scenarios. This methodological improvement overcomes the limitations of previous studies, ensuring that the detection model is trained on diverse and representative data. By incorporating a wider range of manipulations, including subtle retouching techniques such as filters, highlights, resizing, and inpainting, the proposed approach enhances the model's ability to generalize and accurately detect manipulation across various contexts. Furthermore, our approach capitalizes on established passive detection methods within a multi-stream neural network architecture, as outlined in the abstract. This integration enables the utilization of knowledge acquired from traditional detection methods to enhance the accuracy of neural network-based approaches. By leveraging this synergy, our method aims to significantly mitigate overfitting, a prevalent challenge in such methodologies. Besides this introductory section, the following sections aim to explain better how the proposed approach works; in Section 2, we discuss several relevant related works. Section 3 presents the methods, the dataset used, and the model architecture; then, Section 4 discusses the results we obtained. Finally, Section 5 presented the final considerations and suggestions to improve the approach.
###### Abstract  
This study designed a multi-functional reconfigurable transmitarray with switchable polarization and beam steering capabilities using only a PIN diode having ON/OFF switching ability. The simulation confirmed that the proposed architecture is capable of 2D beam scanning of up to \(\pm 45^{\circ}\) for elevation and azimuth while maintaining or converting linear polarization when operating within the X-band. The performance was verified through a far-field beam pattern measurement using the near-field antenna measurement system by producing a transmitarray with an \(8\times 8\) unit cell arrangement. The maximum level of gain of the proposed architecture was confirmed to be 14.5 dBi, the sidelobe level was below 14.5 dBc, the polarization converting loss was below 1.28 dB, and the polarization isolation level was above 14.7 dB. It can be utilized for the design technique of a radar system demanding small size, weight, power, and cost (SWaP-C) characteristics based on a simplified composition.  
transmitarray; switchable polarization; beam steering; SWaP-C
## 3 Related Work
### Methods Used in the Indoor People Location Service  
The analyzed systems leverage the received signal strength indicator (RSSI) from Wi-Fi networks or Bluetooth beacons, which transmit low-energy signals to nearby mobile devices without prior synchronization. Studies [11; 12; 16; 18; 22; 24] store Wi-Fi RSSI data in a database along with the basic service set identifier (BSSID) and the point of interest where the signal was captured, a technique known as fingerprinting. For location determination, [11; 18; 21; 24] apply classification algorithms such as K-nearest neighbors (KNN) or naive Bayes. Systems using beacon-based RSSI include [13; 14; 15; 17; 19; 25]. Notably, ref. [21] combines Wi-Fi and beacon signals to ensure location tracking even when one signal type is unavailable. The hybrid approach in [17] integrates PDR navigation with real-time beacon RSSI analysis. These systems rely on RSSI fingerprinting for user localization through Wi-Fi access points or Bluetooth beacons. However, while some studies measure only three fingerprints per meter, tests suggest capturing approximately ten RSSI readings per point of interest to enhance location accuracy for victims within a building.
## 3 Results
### RQ3: How Should Psychometrics Assess Emotional Experience?
#### 3.4.3 Timing Protocol and Procedure  
The time of stimulus presentation was highly variable across studies: 0.8 s, [28], 1 s [42; 52; 53; 58], 1.5 s [47; 65], 2 s [35], 2.5 s [63], 3 s [39; 61; 64], 4 s [25; 30; 33; 34; 37; 54], 5 s [32; 38; 49; 55; 57; 66], 6 s [30; 31; 36; 41; 45; 50; 56; 60], 7 s [40], 8 s [36; 50], 10 s [36; 44], 15 s [32], 45 s [43], 1 min [26; 48], 1.2 min [62], 1.5 to 2 min [27; 29], 3 min [46], 3.5 min [51], 3 to 5 min [59] and 4 min [26]. Figure 5 details the presentation time according to stimulus types.  
RQ5: How May Correlates of Emotion Processing Be Applied to Models of Statistical Analysis and Artificial Intelligence?  
In biomedical areas, statistical modelling plays an important role throughout a study, from the planning stages to data interpretation. The latter is a crucial step that dictates the research outcomes. Therefore, an adequate selection of statistical tests clearly determines valid inferences and conclusions. To choose the optimal test, it is necessary to look for the data distribution, sample size, homogeneity of variances and/or covariances, and dependency between variables [71; 72].
## 2 Materials and Methods
### Methods and Algorithms
#### 2.3.2 Direct Kinematics  
Direct kinematics allows the position and orientation of the tool structure to be determined given all its manipulator joint parameters and the respective joint angle and distance. A convention commonly used to calculate the parameters required in the kinematic model in robotics applications is the Denavit-Hartenberg (DH) convention, which has four parameters [50] associated with the link and joint:  
* \(\mathrm{a_{i}}\): link length;
* \(\mathrm{a_{i}}\): link torsion;
* \(\mathrm{d_{i}}\): offset;
* \(\mathrm{\theta_{i}}\): joint angle.  
This convention gives rise to an individual homogeneous transformation matrix as the product of four basic transformations [51], as presented in Equations (1) and (2):  
\[\mathrm{A_{i}}=\mathrm{Rot}_{\mathrm{Z},\mathrm{\theta_{i}}}\cdot\mathrm{ Trans}_{\mathrm{Z},\mathrm{d_{i}}}\cdot\mathrm{Trans}_{\mathrm{X},\mathrm{a_{i}}} \cdot\mathrm{Rot}_{\mathrm{X},\mathrm{\alpha_{i}}}, \tag{1}\]  
Performing the multiplications of the matrices, the DH matrix is obtained [51]:  
\[\mathrm{A_{i}}=\begin{bmatrix}\mathrm{C_{\theta_{i}}}&-\mathrm{S_{\theta_{i}} }\cdot\mathrm{C_{\alpha_{i}}}&\mathrm{S_{\theta_{i}}}\cdot\mathrm{S_{\alpha_{ i}}}&\mathrm{a_{i}}\cdot\mathrm{C_{\theta_{i}}}\\ \mathrm{S_{\theta_{i}}}&\mathrm{C_{\theta_{i}}}\cdot\mathrm{C_{\alpha_{i}}}&- \mathrm{C_{\theta_{i}}}\cdot\mathrm{S_{\alpha_{i}}}&\mathrm{a_{i}}\cdot\mathrm{ S_{\theta_{i}}}\\ 0&\mathrm{S_{\alpha_{i}}}&\mathrm{C_{\alpha_{i}}}&\mathrm{d_{i}}\\ 0&0&0&1\end{bmatrix}, \tag{2}\]  
Equations (1) and (2) refer to the position and orientation of the link in the space, where \(\mathrm{A_{i}}\) is the transformation matrix, \(\mathrm{S}\) is sine, \(\mathrm{C}\) is cosine and \(\mathrm{i}\) is the number of degrees of freedom. For \(\mathrm{i}\) degrees of freedom, the position and orientation of the tool structure relative to the inertial structure (fixed referential) are given by a homogeneous transformation matrix [50] represented in Equation (3):  
\[\mathrm{A}=\begin{bmatrix}\mathrm{R_{i}^{0}}&\mathrm{O_{i}^{0}}\\ 0&1\end{bmatrix}=\mathrm{T_{i}^{0}}(\mathrm{q_{1}},\ldots\mathrm{q_{i}})= \mathrm{A_{1}}(\mathrm{q_{1}})\cdot\mathrm{A_{2}}(\mathrm{q_{2}})\cdot \mathrm{A_{3}}(\mathrm{q_{3}})\ldots\mathrm{A_{i}}(\mathrm{q_{i}}), \tag{3}\]  
Figure 3: Representation of the homogeneous matrix.  
where A is the homogeneous matrix (position and orientation of the effector in relation to the base), R is the rotation matrix, q is the angle or displacement associated with the joint, and O is the translation matrix.  
The homogeneous transformation can be represented using the 4 DH parameters. To achieve this, it is necessary to define and establish rules for some references presented in the conditions given by Equations (4) and (5) [51]:  
\[\hat{X}_{i+1}\ \perp\hat{Z}_{I}, \tag{4}\]  
\[\hat{X}_{i+1}\bigcap\hat{Z}_{I}\text{ para i}>0, \tag{5}\]  
Equations (4) and (5) are rules to determine the kinematics of the manipulator.
###### Abstract
That is to say that in practical applications, systems may be subject to exogenous disturbances [28], which can lead to unavoidable instability. Consequently, the consensus problem of multi-agent systems with exogenous disturbances has garnered considerable attention. Investigating such issues enhances the practical relevance of this research study. Building on the aforementioned ideas, we concentrate on the issue of observer-based prescribed-time bipartite output consensus for nonlinear multi-agent systems in both the absence and presence of exogenous disturbances. The most important contributions and innovation points of this study are described as follows:  
1. In contrast to previous studies on bipartite consensus, which necessitate a network topology to achieve a balanced state [29], in this study, we demonstrate that the implementation of cooperation and competition does not require such a topology to attain a balanced condition. 2. Compared with [30; 31], this study broadens the discussion to encompass more complex nonlinear multi-agent systems, which is more practical. Furthermore, it placesparticular emphasis on scenarios involving exogenous disturbances, thereby enhancing the generality of the model. 3. With reference to [32], state observers and disturbance observers are designed to effectively estimate their actual values, allowing for the extension of consensus from the previously established fixed- and finite-time frameworks to a prescribed-time consensus approach. The paper is organized as follows: In Section 2, we present some preparatory knowledge on notations and graph theory. In Section 3, we expound upon the main theorem. Next, an example is provided in Section 4 to validate the effectiveness of the results. Finally, in Section 5, we present the conclusions of the study and propose future research directions.
## 3 Results
### Wedelolactone Contents and Antioxidant Activities of the Extracts  
As shown in Figure 2, the wedelolactone amount of 378.6 mg/100 g in the UE was higher than that of 179.2 mg/100 g in the EE from the conventional extraction process. This amount was two times higher than that in ultrasonic process, as well as that in the other reported results in the range of 100-400 mg/100 g [23; 24]. This result indicates that the UE may play an important role in improving the extraction yield of wedelolactone because of the minimal destruction of heat-labile wedelolactone caused by the low temperature extraction process, even at low temperature [25]. Therefore, the extract from the ultrasonic process is expected to have good antioxidant activities because wedelolactone is a family of coumarin known to have high antioxidant activity and anti-inflammatory efficacy [15; 26].  
Figure 1: Cytotoxicity of the _E. prostrata_ L extracts against human skin fibroblasts (CCD-986 sk). Mean values \(\pm\) Standard Deviation (SD) from triplicate separated experiments are shown. Means with different letters (A–B) within the same sample are significantly different at \(p\) < 0.05 and means with different letters (a–b) within the same concentration are significantly different at \(p\) < 0.05. (1) EE: 70% ethanol extraction at 80 \({}^{\circ}\)C, (2) UE: ultrasonic extraction with 70% ethanol at 40 \({}^{\circ}\)C.  
Figure 3 compares the antioxidant activities of the extracts from two processes: The EE showed a scavenging activity level of 65.21% at a maximum concentration of 0.3 mg/mL while the UE exhibited an activity level of 70.35%. This result strongly implies that the DPPH free radical scavenging activity of the _E. prostrata_ L. extract was increased through the ultrasonic extraction process. Moreover, the scavenging activity of the UE was a little lower than or even similar to that of ascorbic acid, a positive control in measuring antioxidant activities. Compared with 62.98% of the DPPH radical scavenging activity in the addition of 1.0 mg/mL of _Agastache rugosa_ O. _[PERSON]_ extract that has similar biological activities, even though this plant did not contain the same bioactive substance, wedelolactone [27], the UE of the _E. prostrata_ L. extract showed a higher scavenging activity of 70.35% at a concentration of 0.3 mg/mL.  
Figure 2: Comparison of High Performance Liquid Chromatography (HPLC) chromatograms of _E. prostrata_ L. extracts from two different extraction processes. **(A)** Wedelolactone, a standard, **(B)** EE: 70% ethanol extraction at 80 \({}^{\circ}\)C, **(C)** UE: ultrasonic extraction with 70% ethanol at 40 \({}^{\circ}\)C.  
In previous studies that identified the antioxidant effects of _E. prostrata_ L. extract, active oxygen was indicated to induce inflammation through various media, such as internal lipids and proteins. Therefore, the antioxidant activity of the _E. prostrata_ L. extracts is considered to be associated with anti-inflammatory efficacy [11]. Attempts have been made to prove its anti-inflammatory efficacy--specifically the effects on human skin anti-inflammation for cosmetic applications.
# A Survey of Defensive Measures for Digital
## 3 The Scope: Examples of Digital Persecution in the Global South
### India  
Network Traffic Analysis (NETRA) is the Indian internet surveillance system. A 2016 case study of this system explains that it is used to target people and groups \"that have a history or an inclination to carry out heinous and opprobvious acts\" [23]. It filters internet traffic looking for words, such as \"attack\", \"kill\", \"bomb\", etc. It then reports the usages of the word and the associated IP address. NETRA monitors communication done via email, Google Talk, Facebook, Skype, Blackberry, instant messages, as well as blogs and forums. It can even analyze some encrypted traffic. However, the system is unable to discern the meaning or context of a word and it must rely on pre-defined filters. This means that an individual could erroneously become an object of suspicion.
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 3 Methodology
### Stride-TCN  
The study focused on energy consumption and was tested on three datasets related to energy consumption. The data analysis found that the energy consumption data are seasonal (Figure 2). If we break down the time series into small components based on that period and compare them, we observe the similarity in the shape of the time series. Therefore, the information in the same positions in the periods has a strong relationship and supports making predictions for the next periods.  
The proposed architecture extracts and learns the information at the corresponding locations of each related period, where the model is efficient and reduces the model complexity. Similar to the TCN, the proposed architecture consists of two components: dilated and causal 1D convolutional layers. However, the proposed model was adjusted in the calculation of 1D convolution. The layer \(L_{n}\) is calculated directly based on the lower layer \(L_{n-1}\). Node \(X_{i}^{L_{n}}\) in layer \(L_{n}\) is determined by convolution through a kernel of size \(K\) sliding over the layer \(L_{n-1}\). Node \(X_{i+1}^{L_{n}}\) in layer \(L_{n}\) is calculated in the same way as \(X_{i}^{L_{n}}\), but the convolutional position on layer \(L_{n-1}\) must be at a distance \(S\) from the position of the calculated node \(X_{i}^{L_{n}}\). This distance is considered the time dependence of the data mentioned above.  
The autocorrelation method is used to determine the time dependence \(S\) of the data series [15]. We call the model built by this approach heuristic-stride-TCN. Although the model parameters were reduced, the model error was still high compared to the TCN method.  
In another approach to determining the time dependence \(S\) of the data series, we propose the stride-TCN architecture by applying Bayesian optimization to determine the hyperparameters automatically. We reduced model parameters and errors. Table 2 lists the hyperparameters automatically searched for in the stride-TCN.  
An overview of stride-TCN architecture is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Symbol** & **Choices** \\ \hline Kernal size & \(K\) & \(1,3,5,7,9\) \\ Number of filters & \(N_{i}\) & \(8,16,32,64,128,256,512\) \\ Stride & \(S\) & \(1,2,3,\ldots 24\) \\ Dropout rate & \(\rho\) & \(0,0.1,\ldots\ 0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Search space of stride-TCN used by Bayesian optimization.  
Figure 2: Sample in Chonnam National University energy consumption.
## 3 Simulation Model
### Second Stage--Routing Path Application
#### 3.2.1 Edge Node Simulator  
Edge nodes connect multiple subnetworks running on top of legacy link layer protocols to the OBS network. They can be considered either ingress or egress nodes. When acting as ingress node, edge nodes are responsible for aggregating the incoming packets into bursts, for taking the initial (and here also permanent) routing decision, and for scheduling the bursts for transmission on outgoing channels. In our model, we assume the burst as the basic transport unit of interest. Hence, the issues of packet aggregation and burst assembly are considered out of scope. It is worth noting that the traffic generator  
Figure 8: Screenshots of the OBS simulator (simplified view).  
developed here is a burst generator, generating messages based on a Poisson process with a symmetric all-to-all traffic matrix. Thus, whenever a Poisson process timer expires, a new burst is generated, a destination address is chosen at random between all other nodes in the network, a route to the destination node is taken from the source nodes routing table, and an initial wavelength is selected among the free ones. The model uses source-based routing, which is our first way of addressing contentions with an _a priory_ action on the space domain. The burst, together with all its relevant information, is then retained in a system queue organized by destination address, and the connection-setup process starts with the sending of a CP on the appropriate dedicated channel. The CP is always transmitted before the corresponding burst and separated from it by the adequate offset time. The model calculates this offset time in order to allow the CP to be processed at each subsequent node, before the burst arrival, and in such a way that an optical path can be properly reserved for burst delivery.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Datasets, Implementation Details, and Evaluation Protocols
#### 4.1.3 Evaluation and Data Augmentation  
This section mainly the evaluation metric, training strategy, and data augmentation for the proposed algorithm, which is described as below, respectively.  
Evaluation protocol: We adopted the widely used cumulative match curve (CMC) approach [25] for quantitative evaluation. This measure indicates how the matching performance of the algorithm improved as the number of returned image increases. The second measure manner is the mean average precision (mAP) [24], which reflects the overall ranking quality rather than looking at top \(n\) positions only.  
Data Augmentation: In the training set, the matched sample pairs (positive samples) are far fewer than the mismatched pairs (negative samples). If they are directly used to train the deep network, the model tends to predict all inputs as being non-matched. The most common method to avoid this issue is to enlarge the positive samples and randomly reduce the negative ones by label-preserving transformation artificially. In this study, we used a data augmentation strategy to extract random patches from previous image pairs [20]. For an original image of the size \(w\times h\), we sampled ten identically sized images around the center in the range \([-0.05w,0.05w]\times[-0.05h,0.05h]\).
## 2 Background
### An Overview of Federated Learning
#### 2.2.4 Federated Learning Systems Taxonomy
* Data evolution: The evolution of data in FLS taxonomy pertains to the temporal modifications that transpire within the datasets maintained by participants. These modifications can have noteworthy consequences in terms of the efficiency and precision of FL models. In certain FL situations, the data remain static throughout the FL process, resulting in a simplified training procedure. Static data are typically seen in situations where the underlying data exhibits few changes, as in the case of historical datasets or reference databases. Notably, dynamic data have the potential to change over time. Consequently, participants may find it necessary to update their respective local datasets regularly.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Effect of Other Co-Existing Ions  
The investigation of the effect of coexisting ions is most important in adsorption studies as industrial wastewater consists of different anions and cations. There may be rivalry among the different ions for the same adsorption sites. It is preferable to prepare a multifunctional sorbent capable of extracting different forms of metal ions. [PERSON] et al. [100] synthesised poly(ethylene glycol dimethacrylate-acrylamide) copolymer for extraction of (lead mercury and cadmium) metal ions in an aqueous solution and the order of metaluptake was found to be lead > cadmium > mercury. For a 1.0 mg/L concentration of each metal, it was discovered that cadmium adsorption was higher in the mixture (0.52 mmol) than in the individual solution (0.37 mmol) [100].