esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
The electromagnetic domain faces notable challenges from adversarial attacks. [PERSON] et al. [18] first identified adversarial impacts on signal recognition. Subsequent studies highlighted the reduction in communication efficiency in IoT systems due to these attacks [19] and vulnerabilities in RF fingerprinting [20; 21; 22]. Practical implications were explored by [PERSON] et al. [23] who proposed BER as an evaluation metric. Research also delved into the influence of gradient-based attacks on modulation recognition [24] and the vulnerabilities in CNN-based device identification [25].  
From these observations, it is evident that the electromagnetic domain is severely impacted by adversarial attacks, affecting communication processes, modulation recognition, and reliable transmission. This jeopardizes regular communication security and privacy protection. Adversarial examples have thus evolved into a widely acknowledged threat within the electromagnetic domain. The threat posed by adversarial examples to applications in the electromagnetic field is depicted in Figure 1.  
To address the challenges posed by adversarial attacks, researchers in the signal domain have introduced various defense techniques. Examples include the adversarial training method proposed by [PERSON] et al. [26], the multi-layer defense mechanisms by [PERSON] et al. [27], and the stochastic smoothing approach by [PERSON] et al. [28]. Notably, these techniques are largely aimed at enhancing model robustness. However, they often require retraining or modifications to the model's architecture, which could affect its performance and practicality.  
Recent advancements in adversarial example detection have leveraged AE, with initial success in computer vision. [PERSON] et al. [29] utilized intermediate layers of target networks to detect adversarial examples using AE. [PERSON] et al. [30] integrated Gaussian, mean, and median filtering with AEs for image adversarial detection. [PERSON] et al. [31] proposed the FADetector using feature knowledge. In the realm of RF signals, [PERSON] et al. [22] took a statistical approach toward adversarial detection, although with a constrained experimental scope.  
In essence, while AE-based techniques show promise, direct applications from the image to signal domains are ineffective. There is an emerging need for AE structures tailored to signals and broader validation across modulated signal datasets.
## 2 Materials and Methods
### Supplementation of Ex-Situ Biofloc to Shrimp Grow-Out Culture at Low Salinity Condition  
Nine tanks containing 70 L of 10 ppt water were used as the experimental culture units. A group of fifteen-day old post-larval shrimp (PL15) with initial average body weight of 0.0057 \(\pm\) 0.003 g was distributed randomly to the experiment tanks at a density of 500 shrimps/m\({}^{3}\). Commercial feed ([PERSON] shrimp feed, PT. Matahari Sakti, Indonesia) was provided four times daily for six weeks of the culture period. Temperature, pH, salinity, alkalinity, dissolved oxygen and biofloc volume were maintained at the optimum range for shrimp growth [14].  
Three treatment groups were tested with three replicate tanks per treatment: (1) commercial pellet treatment without biofloc supplementation (100%C); (2) 5% (\(w/v\)) feed reduction supplemented with 0.3% (\(v/v\)) biofloc (95%C+BF); and (3) 10% (\(w/v\)) feed reduction supplemented with 0.3% (\(v/v\)) biofloc (90%C+BF) (Figure 1). The ex-situ biofloc was delivered every week and freshwater was regularly added to make up for water loss due to evaporation. Water quality (DO, pH, salinity and alkalinity) was measured weekly using a portable DO meter, a pH meter, a refracometer and titration, respectively. Total ammonium nitrogen, NO\({}_{2}\)-N, NO\({}_{3}\)-N, and orthophosphate were measured daily following the procedures in the Standard Methods for the Examination of the Water and Wastewater [26].  
Shrimp growth performance was evaluated. The final biomass was calculated as the total weight of live shrimp at the end of cultivation. Gained weight and length were measured by calculating the difference between final weight/length and initial weight/length. Specific growth rate (SGR) was calculated with this formula:  
\[\text{SGR }(\%/\text{day})=\frac{\text{In }(\text{final weight/initial weight})\times 100\ \%}{\text{Day of cultivation }(\text{days})}\]  
At the end of the experiment, survival was measured as the percentage of live shrimp on the final day relative to the total initially stocked shrimp. Shrimp muscle was dissected, quickly frozen in liquid nitrogen and stored at \(-80\ ^{\circ}\)C prior to growth-related gene expression and nutritional analyses.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Conditionally Deep Autoencoders
Finally, CDMMA's output, \(\mathcal{D}(y;\mathcal{M})\), is given as  
\[\widehat{\mathcal{D}}(y;\mathcal{M}) =\hat{y}^{l^{*}}(x^{l^{*}};\mathbb{M}^{l^{*}}) \tag{30}\] \[l^{*} =\arg\min_{l\in\{1,\cdots,L\}}\ \|y-\hat{y}^{l}(x^{l};\mathbb{M}^{l})\|^{2}. \tag{31}\]  
For a big dataset, the computational time required by Algorithm 2 for learning will be high. To circumvent the problem of large computation time for processing big data, it is suggested in [15; 22] that the data be partitioned into subsets and corresponding to each data subset, a separate CDMMA is learned. This motivates the defining of a wide CDMMA as in Definition 6. For the variational learning of wide CDMMA, Algorithm 3 follows from [15; 22], where the choice of number of subsets as \(S=\lceil N/1000\rceil\) is driven by the consideration that each subset contains around 1000 data points, since processing the data points up to 1000 by CDMMA is not computationally a challenge. **Definition 6** (A Wide CDMMA [15; 22]). : _A wide CDMMA, \(\mathcal{WD}:\mathbb{R}^{p}\to\mathbb{R}^{p}\), maps a vector \(y\in\mathbb{R}^{p}\) to \(\mathcal{WD}(y)\in\mathbb{R}^{p}\) through a parallel composition of \(S\) (\(S\in\mathcal{Z}_{+}\)) number of CDMMAs such that_  
\[\mathcal{WD}(y) =\mathcal{D}_{s^{*}}(y) \tag{32}\] \[s^{*} =\arg\min_{s\in\{1,2,\cdots,S\}}\ \|y-\mathcal{D}_{s}(y)\|^{2}, \tag{33}\]  
_where \(\mathcal{D}_{s}(y)\) is the output of the \(s\)-th CDMMA._  
```
0: Dataset \(\mathbf{Y}=\left\{y^{i}\in\mathbb{R}^{p}\ |\ i\in\{1,\cdots,N\}\right\}\); the subspace dimension \(n\in\{1,2,\cdots,p\}\); ratio \(r_{max}\in(0,1]\); the number of layers \(L\in\mathbb{Z}_{+}\). 1: Apply k-means clustering to partition \(\mathbf{Y}\) into \(S\) subsets, \(\{\mathbf{Y}^{1},\cdots,\mathbf{Y}^{S}\}\), where \(S=\lceil N/1000\rceil\). 2:for\(s=1\) to \(S\)do
3: Build a CDMMA, \(\mathcal{M}^{s}\), by applying Algorithm 2 on \(\mathbf{Y}^{s}\) taking \(n\) as the subspace dimension; maximum number of auxiliary points as equal to \(r_{max}\times\#\mathbf{Y}^{s}\) (where \(\#\mathbf{Y}^{s}\) is the number of data points in \(\mathbf{Y}^{s}\)); and \(L\) is the number of layers. 4:endfor
5:return the parameters set \(\mathcal{P}=\{\mathcal{M}^{s}\}_{s=1}^{S}\). ```  
**Algorithm 3** Variational learning of wide CDMMA [15; 22]  
**Definition 7** (Wide CDMMA Filtering [15; 22]). : _Given a wide CDMMA with its parameters being represented by a set \(\mathcal{P}=\{\mathcal{M}^{s}\}_{s=1}^{S}\), the autoencoder can be applied for filtering a given input vector \(y\in\mathbb{R}^{p}\) as follows:_  
\[\widehat{\mathcal{WD}}(y;\mathcal{P}) =\widehat{\mathcal{D}}(y;\mathcal{M}^{s}) \tag{34}\] \[s^{*} =\arg\min_{s\in\{1,2,\cdots,S\}}\ \|y-\widehat{\mathcal{D}}(y; \mathcal{M}^{s})\|^{2}, \tag{35}\]  
_where \(\widehat{\mathcal{D}}(y;\mathcal{M}^{s})\) is the output of the \(s\)-th CDMMA estimated using (30)._
## 3 The Structure of the Decision Support Platform
### Forecasting Consumption Module
The information about the operating modes (on/off), using 1 or 0, the operating cycles, and the working times (reduced, normal, and long), identified through the numbers 1, 2, and 3, for each equipment or installation introduced in matrix EIM, are recorded in this matrix. Based on the information contained in the two matrices, the rules of the expert systems with the logical structure  
**If** (_<equipment or installation e is on>_**and**_<working time is reduced>_**and**_<number of cycles is n\({}_{c}\)_>)_  
**then**_<forecasted consumption is W\({}_{\cancel{>}}\),_  
are applied to each equipment and installation associated with each activity. In the final stage, the total energy consumption for the next day will be obtained using the aggregation of the forecasted consumptions of each equipment and installation \(e\), \(e\) = 1,..., \(n_{e}^{a}\), associated with each activity \(a\), \(a\) = 1,..., \(n_{a}\). Figure 8: The framework of the expert system.
## 4 Experiment Results and Comparison
### Data Sets Used in Experiment
#### 4.1.2 ISCX 2012  
The ISCX dataset 2012, collected at the University of New Brunswick's Information Security Centre of Excellence, will be used as a second dataset to conduct the experiments and assess the performance of the proposed technique. The complete ISCX-labeled dataset contains more than two million traffic packets represented by 20 features. It involves seven days of network traffic (i.e., normal and intrusion), as shown in Table 4. Further description of this dataset can be found in [30]. Despite a few drawbacks, ISCX is still the most up-to-date dataset when compared to other frequently developed datasets.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Normal** & **Dos** & **Probe** & **R2L** & **U2R** & **Total** \\ \hline
**Number of** & 67,343 & 45,930 & 11,656 & 995 & 49 & 125,973 \\
**Occurrence** & 53.5\% & 36.5\% & 9.3\% & 0.8\% & \(\sim\)=0\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the NSL-KDD data set.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 4 Research Methodology
### Textual Representation of PLM Unit  
This unit is responsible for extracting text-based features from posts using the BERT model. It processes pre-processed news posts and comments obtained from the previously described unit and feeds them into BERT models for representation extraction. We opted for PLMs due to their exceptional performance and suitability for small data sizes. As these models are trained on vast amounts of data, they help mitigate the overfitting problem. Furthermore, these models generally perform well in inferring context and implicitly recognizing emotions. This presents an additional challenge when trying to leverage emotional features for rumor identification. As a result, we decided to utilize these models to demonstrate the effectiveness of incorporating emotion features to enhance rumor detection capabilities. We explored two recently developed Arabic PLMs, AraBERT-Twitter, and MARBERT, for extracting textual representations in the context of rumor detection. These models were selected based on their impressive performance across various classification tasks and their training on Twitter data, which aligns with our dataset comprising Twitter comments in colloquial languages.  
Contextual embeddings of textual inputs were acquired using two approaches: fine-tuning the BERT model and employing a feature-based method without fine-tuning any BERT parameters. Comparing the fine-tuning and feature-based approaches for each model can shed light on the best strategy for extracting and utilizing textual and emotional features in the task of rumor detection. Furthermore, we argue that better representation of sentences can affect utilizing emotions. This motivated us to investigate various textual representations through three methods: first, by extracting the hidden state of the last layer; second, by concatenating the hidden states of the last four layers; and lastly, by applying mean pooling on the last four layers to compute the average of all tokens. Padding tokens were immediately removed to avoid generating distinct sentence embeddings based on the number of padding tokens. To extract representations, we tokenized each instance using the HuggingFace library, incorporating [SEP] and [CLS] tags and subsequently encoded them to produce token IDs. For sentences exceeding 512 tokens in length, we truncated them to accommodate language models limited to a maximum of 512 tokens. Conversely, shorter sentences were padded to achieve a uniform size. The resulting output representation consisted of 768 tokens, which collectively represented each sentence.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 4 BSM Reuse Model: Our Proposal
### Architecture Design
#### 4.4.1 Data Capture  
At this first stage, to manage the data transfer process, we adopt our previous Request-To-Receive approach [5] showed in Figure 6, to address the blinding exchange of BSMs and reduces the average number of collected Data Element. Performing this type of validation early on has a positive impact on the bandwidth capacity.  
A Categorization: further, we introduce a new concept of 'data temperature' to categorize the raw data captured accordingly. Hot data represent real-time BSMs and necessitate real-time processing to be more beneficial (i.e., less than a second from receipt to action). Hot data are also cached in a database shown in Figure 5, with red color. Cold data denote offline BSMs and are stored in blue database. Flextime processes can operate with cold data that have been stored. Hot data are simultaneously delivered alone to safety applications and to the storage function along with offline data.  
B Filtration: without any type of filtration, vehicles could easily get flooded with data. Data filtration addresses the issue of uninformative content of received raw BSM. The non-informative content can be real-time detected just by checking whether received BSM holds new data or not. If it is the case, the BSM is stored, otherwise, it comes to non-informative BSM that will be discarded. For instance, if a vehicle travels at more-or-less the same speed and heading during the entire trajectory, the data will essentially unchanged, then there would most probably be no loss of information. Therefore, a proper two-steps algorithm is performedin order to fulfill the categorization and filtration tasks. The algorithm will save valuable information and discard the rest.  
Step 1: the designed algorithm examines the data element DSecond to know whether the message is possessing a real-time or offline data. The period of the message transmission is supervised by the DSecond data element. The later provides a time value when a BSM is populated with data there may  
Figure 5: Basic architectural concept of our BSMs reuse model.  
be a lag between the time the data is collected and populated in the BSM. BSMs are then grouped into two categories: hot and cool. Each of these two groups of data will require different kinds of processing and storage functions.  
Step 2: the algorithm inspects the value of two data elements TemporaryID and the MsgCount of every hot BSM. It discards the BSM having old content but simultaneously notify local safety applications and storage each BSM having new content.
# A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
## 4 Hierarchical Federated Learning Algorithm Based on Time-Effective Aggregation
```
0: Initialize global model parameters \(\omega^{0}\), global epochs \(T\), local epochs \(E\), learning rates \(\eta\), waiting time \(\tau\),
0: Global model \(\omega\);
1:Central server:
2:for\(t=1,2,\ldots,T\)do
3:for each edge server \(m=1,2,\ldots,M\)do
4: Send global model parameters \(\omega^{t}\) to edge server \(m\) in parallel;
5: Receive aggregation model \(\omega^{t}_{m}\) from edge server \(m\);
6:endfor
7: Global aggregation: \(\omega^{(t+1)}\leftarrow\frac{\sum_{m\
eq M}|D_{m}|\omega^{t}_{m}}{|D|}\);
8:endfor
9:Edge server:
10:for each edge server \(m=1,2,\ldots,M\)do
11: Select participating client \(i=1,2,\ldots,C\);
12:for each local client \(i\)do
13: Send global model \(\omega^{t}\) to client \(i\);
14: Set the version number \(\mathit{version}=t\);
15:endfor
16: Edge aggregation: receive the local model parameters \(\omega^{t}_{i}\) from the participating client \(i\) within the waiting time \(\tau\);
17:if\(\mathit{version}=t\)then
18: Aggregation \(\omega^{t}_{(m,S^{\prime})}\) participating in the \(t\)-th round of training;
19:else
20: Aggregation \(\omega^{t}_{(m,S^{\prime})}\) participating in the previous round of training;
21:endif
22: Weighted aggregation of \(\omega^{t}_{(m,S^{\prime})}\) and \(\omega^{t}_{(m,S^{\prime\prime})}\);
23:endfor
```  
**Algorithm 2** The hierarchical federated learning algorithm based on efficient aggregation mechanism (FedEdge)
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 4 Design of the TeamUp Network Based on LoRaWAN
### Mobile Device and TeamUp Application  
The mobile device hosts the application developed for the teams, called TeamUp, and will be used by each member of the team. The interface of this application is shown in Figure 6 and consists of the navigation screens available to the user.  
The internal implementation of the application is organized in four blocks, depending on the functionality. The development was also performed so that it would be compatible with all devices that incorporate an Android version equal to or higher than 4.0.3 (Ice Cream Sandwich). The four blocks implemented are login and registration, Bluetooth Low Energy (BLE) functionality to establish and manage the connection between the smartphone and the device, chat functionality to manage messages on the smartphone and GPS functionality to control the user's positioning.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 3 Toxicology
### Presence and Distribution into the Environment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Country**} & **Proximity to Activity Sources** & **Soil** & **Element** & **Concentration (mg\_kg\({}^{-1}\))** & **Ref.** \\ \hline \multirow{8}{*}{Pakistan (Swabi)} & G = Depth (cm) & Cu & G: 2.33–19.15 & \\  & 0–15 & Cu & H: 1.32–14.11 & \\  & Organic matter (\%) & Fe & G: 8.23–36.89 & \\  & 0.35–2.30 & & H:8.22–30.95 & \\  & pH 7.21–9.21 & Zn & G: 8.26–26.55 & \\  & EC (dS\({}^{-1}\)) & & H: 7.77–24.20 & \\  & 0.13–0.56 & Cd & G: 0.01–0.16 & \\  & CaCO\({}_{3}\) (\%) & & H:0.01–0.08 & \\  & 5.89–16.65 & Co & G: 0.8–6.99 & \\  & H = Depth (cm) 15–30 & & H: 0.33–5.46 & \\  & OMC (\%) 0.21–1.52 & Ni & G: 0.46–22.21 & \\  & pH 7.32–8.88 & & H:0.42–21.9 & \\  & EC (dS\({}^{-1}\)) & Cr & G: 0.23–8.02 & \\  & 0.18–0.86 & & H:0.54–5.11 & \\  & CaCO\({}_{3}\) (\%) & Pb & G: 0.4–2.23 & \\  & 6.56–17.81 & & H:0.08–1.99 & \\ \hline \multirow{8}{*}{Colombia (Sinu River Basin)} & & Cu & 1149 & \\  & & Ni & 661 & \\ \cline{1-1}  & & Pb & 0.071 & \\ \cline{1-1}  & & Cd & 0.040 & \\ \cline{1-1}  & & Hg & 0.159 & \\ \cline{1-1}  & & Zn & 1365 & \\ \hline \multirow{8}{*}{Greece (Argolida)} & & Cu & 28.64 & \\ \cline{1-1}  & & Pb & 13.96 & \\ \cline{1-1}  & & Zn & 45.26 & \\ \cline{1-1}  & & Ni & 253.7 & \\ \cline{1-1}  & & Co & 25.05 & \\ \cline{1-1}  & & Mn & 665 & \\ \cline{1-1}  & & As & 5.89 & \\ \cline{1-1}  & & Cd & 0.26 & \\ \cline{1-1}  & & Cr & 138.4 & \\ \cline{1-1}  & & Fe & 2.90 & \\ \cline{1-1}  & & P & 0.039 & \\ \cline{1-1}  & & K & 0.239 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._were significant, especially in Italy and Greece since the concentrations exceeded 90% of the admissible values.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
### Anthropometrics for Customer-Tailored Design
#### 1.1.1 Wear Database  
An aid in the set goal of optimizing the fit for the mass public (mass customization of fit) is the WEAR database (formerly known as the CAESAR database). This database was set up two decades ago and still stands as a standard, as a collaboration between three NATO countries (USA, the Netherland, and Italy) to collect ergonomic data about the general public for the development of product withing the U.S. Air force, The Society of Automotive Engineers (SAE), and others. In contrast to previous collections and database, Caesar was the first to collect state of the art standardized 3D scans of all the subjects, as well as standardized measurements across continents [11]. This database of 3D scans and measurements contains 4000+ subjects from the Netherlands, Spain, and Northern America [15]. It includes 3D scans in a standardized pose and a list of measurements ranging from the basic demographic parameters (e.g., age, gender, ethnicity, etc.) up to detailed measurements taken by researchers (e.g., weight, chest girth, wrist circumference), and so on. All measurements in the database have been recorded as defined in the ISO 7250 nomenclature [16], as well as in their final report on the project [17].
## 3 Results and Discussions
### Damage Behavior in Interrupted Loading  
Tensile tests were performed with interrupted loading inside an X-ray CT chamber where the test specimen was scanned during the interrupted loading stages to evaluate the damage behavior. For these tests, pure Al4047 batch C was selected due to its relatively more damage-tolerant behavior determined in quasistatic tests as well as residual stress states. Three tests were performed to have reliable results and to assign statistical values to the measured values. Stress-strain curve resulting from one of these tests is shown in Figure 6a.  
The specimen was scanned at four stages: before the start of the test (stage 0), at two intermediate stages (stage 1 and stage 2), and finally after fracture (stage 3). The damage level in the gauge length of the specimen, as scanned by computed tomography is shown in Figure 7. The calculation of a number of defects and mean pore volume was calculated from the CT scans at the four stages and is represented in Figure 6b. Before starting tensile loading, the specimen had 911 pores and a mean pore volume of 44,599 \(\upmu\)m\({}^{3}\). The number of pores went up to 1048 at stage 1, with an accompanying mean pore volume of 45,013 \(\upmu\)m\({}^{3}\), which is a marginal increase in pore volume showing the start of new pore formation while their size is very small. The mean pore volume increased to 45,268 \(\upmu\)m\({}^{3}\) at stage 3 with the number of pores at that stage being 1015 showing a decrease in number due to the start of pore coalescence. After fracture at stage 3, there were only 390 pores while the mean pore volume reached to 62,638 \(\upmu\)m\({}^{3}\) due to the integration of pores.  
Figure 6: (**a**) Representative tensile behavior in interrupted tests for batch C performed inside computed tomography chamber; and, (**b**) development of damage behavior in terms of number of pores and mean pore volume at the interrupted stages.  
The stress-strain behavior in Figure 6a gives information about stress relaxation during the intermediate stages. At stage 1 and stage 2, there is a decrease in stress when the load is removed from the specimen for scanning, where the value of this stress relaxation is more at stage 2 when compared to stage 1. This behavior can be attributed to the motion of dislocations in the material. Higher value of plastic deformation at stage 2 will be having a higher speed of dislocation movement, resulting in increased stress relaxation, which is also a reason of increased fracture strain in interrupted tests as compared to continuous tensile tests, as portrayed in Table 4, where fracture strain increased from 4.05 \(\times\) 10\({}^{-2}\) to 4.36 \(\times\) 10\({}^{-2}\) with an accompanying decrease in ultimate tensile strength. This type of mechanism was also reported recently for stainless steel in [46].