esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis
## 5 Simulation Results
### Effect of Dust Storms Conditions on FSO-PDM/PV-OCDMA System  
The effect of different dust storms (LD, MD, and HD) on the performance of the proposed system is investigated in this part. The BER versus number of channels at 10 dBm and 15 dBm transmitted powers and 20 Gbps data rate under different dust storms is depicted in Figure 8. As the dust storm becomes heavier, the maximum number of allowable channels decreases. At LD, the number of channels that our proposed system can support is 47 at 15 dBm transmitted power and BER \(10^{-9}\) which is decreased to 25 channels under MD and 8 channels under HD at same values of BER and \(t_{p}\). As for lower transmitted power, which is 10 dBm, one can observe that lower number of channels can be used (15 for LD, 9 for MD, and 3 for HD).  
Figure 9 displays the log (BER) values under various dust storms versus the propagation distance for the eight simulated channels. Dust storms cause high attenuation to the information signal during propagation in the FSO channel, which leads to the shortest ranges compared to those achieved under fog conditions. The maximum transmission distance between the transmitter and the receiver under LD is 0.775 km, which decreases by 0.515 km under MD, and further decreases by 0.663 km under HD, which is seen in Figure 9a-c. This is expected as the attenuation value caused by dust storms is 25.11 dB/km, 107.11 dB/km, and 297.38 dB/km, respectively, under LD, MD, and HD, respectively [22].  
Figure 10 shows the Q-factor for the eight channels of our suggested model under dust storms versus the FSO link. As propagation ranges increase, the Q-factor decreases as well under the different dust storms. The values of Q-factor at 0.775 km and under LD for the eight channels are 5.08, 4.51, 4.78, 5, 4.95, 4.56, 5.05, and 5.03, respectively. Approximately the same values of the Q-factor can be achieved at a 0.26 km propagation range under MD and a 0.112 km FSO link under HD. The computed log (BER) and the Q-factor for the eight channels at a propagation range of 0.775 km under LD, 0.26 km under MD, and 0.112 km under HD are given in Table 5.  
Figure 8: BER of FSO-PDM/PV-OCDMA system versus number of channels under dusty conditions.  
Figure 10: Q-factor of FSO-PDM/FV-OCDMA system versus FSO link under **(a)** LD, **(b)** MD, and **(c)** HD.  
Figure 9: log (BER) of FSO-PDM/FV-OCDMA system versus FSO link under **(a)** LD, **(b)** MD, and **(c)** HD.
It must be remarked that the three grape varieties are not cultivated in specific territories but are widespread in the Abruzzo region, and the most prestigious vineries produce all the three kinds of wine. As previously discussed, in _Trebbiano d'Abruzzo, Pecorino_, or _Passerina_ wines labeled as mono-varietal, misrepresentation of the grape variety represents a potential commercial fraud. In this context, the similarity of the grape variety and the common geographical origin make varietal discrimination a challenging issue. It follows that a single analytical technique might not provide sufficient information to differentiate the three wine varieties. To overcome such potential limitations, we characterized the three wine varieties by means of different, and somewhat complementary, analytical methods. In particular, a headspace (HS)-SPME method coupled with gas chromatography-mass spectrometry (GC-MS) was used to collect the aroma profile of the wine samples, whereas polyphenols and organic acid profiles were determined by means of ultra-high-performance liquid chromatography (UHPLC). In addition, the concentration of 14 elements (Ba, Ca, Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr, V, and Zn), previously determined by inductively coupled plasma optical emission spectrometry (ICP-OES) [22], was considered. Further, a panel of experts conducted a sensory analysis on representative wine samples extracted from the same pool subjected to chemical analysis. Despite the fact that correlation between chemical data and sensorial properties is an attractive issue in the context of wine research, it must be noted that sensory terms used to describe the wines are generic (e.g., fruity, fresh) and are unsuitable as descriptors for the prediction of the grape variety [2]. To improve the discrimination of the three wine varieties, we combined chemical information provided by the three above-cited analytical techniques and processed the multi-block data by means of some innovative chemometric strategies. In particular, the experimental data obtained by UHPLC, ICP-OES, and GC-MS were handled by two multi-block approaches: Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) [23] and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA) [24]. The outcomes of the data-fusion strategies were compared to the results obtained by applying a common classification method, such as PLS-DA, on the individual experimental data blocks.
## 6 Discussion
### Project Benefits  
A primary aim of the overall project was to help strengthen the UK's quantum technology industry. It has achieved that aim. The benefits of the project are as follows:  
* Characterisation and construction of new semiconductors for use as single-photon sources and single-photon detectors for FSO-QKD systems.
* Investment in the UK's semiconductor packaging capability.
* Engineering of an optical system for FSO-QKD.
* Construction of a control system for FSO communication and QKD.
* Engineering of a standard-compliant key management software.
* The development of the new IP for amplifying cryptographic keys and using a PUF for device identity when establishing DPNs.
* Eliminating the need for certificate-based security for V2X systems.
* Continuous key generation to protect communications within the IoT, in this case, vehicular communication.
* Commercial growth of participating organisations (economic growth and size).
* Creation of a network of organisations able to work together in the quantum technology field for future 6G applications.  
This practical testing has laid the foundation for further experiments, aiming to integrate QKD systems into Internet of Things (IoT) applications. These types of practical experiments with the building blocks of future hyperconnected systems are needed to learn about real-world issues that are not experienced in simulations.
# Privacy and Security in Cognitive Cities: A Systematic Review
## 4 Discussion
### Regulations and Digital Evidence  
As we have discussed before, Ubiquitous Computing makes user awareness very complex. In addition, data protection regulations such as the European Union's General Data Protection Regulation (GDPR) make consent and awareness mandatory prior to data processing. This safeguard does not only apply to user-generated data, but also to derived secondary data, which is inferred from prior users' information, e.g., by ML algorithms. The fact that, in a cognitive city, data might be gathered opportunistically, and that GDPR requires a legal basis prior to data processing, makes it hard to balance the fulfillment of legal obligations with the development of innovative ubiquitous services. Technical and regulatory mechanisms should be developed to boost user awareness and guarantee consent across the ubiquitous landscape of cognitive environments. Furthermore, regulation complexity may grow as it is expected that cognitive city suppliers (e.g., cognitive system manufacturers, operators, and city services contractors) will be accountable under the laws of the _target_ city territory or country, and that these laws may be in conflict with those of the supplier's country. Moreover, due to the diversity of technologies and scenarios that may arise in the cognitive city environment, legal complexity can become intractable. In order to improve law enforcement across different jurisdictions and technologies, we suggest the development of an inter-operable markup policy language and protocol, so devices can automatically exchange data protection policies.  
In the literature, liability has being identified as a concern. Hence it is expected that procedures for gathering evidence from massive amounts of data from cognitive systems will need international standardized procedures and techniques so as to capture, store, and process evidence data. This issue is particularly relevant because cognitive cities may become targets of cybercrime and terrorism. Blockchain is a promising technology to build a transnational trust system for digital evidence. In this sense, the European Union is currently developing several initiatives around technologies to enhance the fight against crime and terrorism, such as the _LOCARD_ project, which aims to provide a platform for chain of custody assurance along the forensic workflow, through a blockchain-based trusted distributed platform allowing the storage of digital evidence metadata [94].
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 3 Fast Connectivity Construction Framework  
Time-constrained communication for managing assets and goods in the on-site production and logistics sectors is one of the essential demands on reliable management to avoid faults and improve the overall manufacturing automation efficiency for the smart factory. Each device for mobile social group communication needs to continuously connect with each other and the Internet. Thus, D2D network connectivity should be constructed all the time to tightly and intelligently deal with low latency against mobility. This section proposes a fast connectivity construction framework based on the DNN-based cognitive computing.  
As mentioned in the analyses of related work, it is not possible to fulfill the offline training for predicting information of D2D networking connectivity since D2D networks are frequently detached from the mobile edge computers where the offline training is performed. This is because that D2D networks can be configured out of the mobile network coverage even via outband signaling. Therefore, the proposed fast connectivity construction framework is designed with on-node DNN-based learning. It means that a smartphone should be able to perform context acquisition, DNN computation, and intelligent actions to satisfy the low latency requirement against mobility. The proposed DNN is a light-weight neural network, not taking high computation power and trained by a not large amount of data batch.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Decoder and Analysis Layer
#### 3.4.1 Multimodal Transformer Decoder  
Each decoder layer consists of self-attention, cross-modal attention, and feed-forward networks (FFN) with residual connections and normalization. For the \(l\)-th layer (\(1\leq l\leq L_{\text{dec}}\)):  
**Self-Attention with Causal Masking**: Processes the decoder's input sequence (e.g., text tokens) while ensuring autoregressive properties:  
\[\mathbf{h}_{l}^{\text{self}}=\text{LayerNorm}(\mathbf{h}_{l-1}+\text{MHSA}( \mathbf{h}_{l-1})), \tag{15}\]  
where MHSA uses a causal mask to prevent future token leakage [42].  
**Cross-Modal Attention**: Fuses visual features \(\mathbf{z}_{L}\in\mathbb{R}^{N\times D}\) (from the encoder) with textual features:  
\[\mathbf{Q}=\mathbf{h}_{l}^{\text{self}}\mathbf{W}_{q},\quad\mathbf{K}= \mathbf{z}_{L}\mathbf{W}_{k},\quad\mathbf{V}=\mathbf{z}_{L}\mathbf{W}_{v}, \tag{16}\]  
\[\mathbf{h}_{l}^{\text{cross}}=\text{LayerNorm}\Bigg{(}\mathbf{h}_{l}^{\text{ self}}+\text{softmax}\Bigg{(}\frac{\mathbf{Q}\mathbf{K}^{\top}}{\sqrt{d_{k}}} \Bigg{)}\mathbf{V}\Bigg{)}. \tag{17}\]  
Figure 5: The figure shows a typical analysis layer (decoder) structure, which adopts the Transformer design.  
**Feed-Forward Network (FFN)**: Applies a nonlinear transformation:  
\[\mathbf{h}_{l}=\text{LayerNorm}(\mathbf{h}_{l}^{\text{cross}}+\text{FFN}( \mathbf{h}_{l}^{\text{cross}})), \tag{18}\]  
where \(\text{FFN}(\mathbf{x})=\text{GeLU}(\mathbf{x}\mathbf{W}_{1}+\mathbf{b}_{1}) \mathbf{W}_{2}+\mathbf{b}_{2}\) with \(\mathbf{W}_{1}\in\mathbb{R}^{D\times 4D}\), \(\mathbf{W}_{2}\in\mathbb{R}^{4D\times D}\).
## 5 Discussion
### Future Development Directions  
For future developments, there are several potential directions to enhance and expand the platform's functionality, such as the following:  
* Training and coaching: In the future, we aim to incorporate online training of the employees in the field of OSH, supplemented by engaging presentation materials (such us game technology-based safety training platforms). This approach is expected to enhance understanding, making the concepts more accessible to all employees and improving the overall learning experience. Moreover, in order to complete the statistical analysis of the experimental data, we intend to use regression models and also SEM.  
Integrating or connecting via APIs with HR platforms:  
This would primarily enable an expansion of the external prevention and protection services' client portfolio, as well as a unified data management. It would also be useful to integrate or connect other applications and platforms used by companies.  
Collaboration features:  
Collaborative workspaces: Create virtual working libraries where users can share documents and discuss and manage various OSH-related projects.  
Notifications and alerts: Develop multiple types of notifications and alerts, aiming to make them customizable for important goals and deadlines.  
Analytics and artificial intelligence:  
Predictive analytics: Use artificial intelligence algorithms to provide real-time predictive analytics and recommendations to users.  
Virtual Assistants: Integrate virtual assistants to answer users' questions and provide support in using the platform.  
Support and community:  
Extensive Support: Create a detailed platform guide, video tutorials and a live chat, all with the aim of providing the most punctual support to users' requests.  
Online community: Expand the existing online community and assign a specialized moderator to oversee it. Within the online community, users can exchange experiences, solutions and best practices related to SSM, thereby enriching the platform and fostering a collaborative environment.  
These are just a few of the development directions that could significantly transform the SSM.ro platform into an essential tool for optimizing the management and effectiveness of occupational health and safety in various industries. By doing so, the platform would provide exponentially significant value to its users.
## 3 Operation and Models of the VANET Testbed
### Network Models
#### 3.2.1.1 VANET Mobility Model  
Of the four categories of VANET mobility models described previously, we adopted both the macroscopic and microscopic models to allow the modeling of vehicle-to-vehicle and vehicle-to-infrastructure interaction. VanetMobiSim is chosen to integrate these models in OPNET due to its flexibility to manipulate output files (by coding its output generator file to produce a format desired by our simulator). It also generates traces in a special Universal Trace Format [15] in the form of (_time node_id pos_X pos_Y velocity acceleration_) where _time_ records the simulated time; _node_id_ identifies the specific node; pos_X_ and _pos_Y_ provide the X and Y coordinates of the specific node at the given time respectively; and the _velocity_ and _acceleration_ show respectively the velocity at which the node moves and its acceleration at that time. With these capabilities, VanetMobiSim can model traffic lights, stop signs, human mobility dynamics and safe inter-distance management. It allows the integration of road topologies from databases such as GDF [51] or TIGER [52] to user-defined topologies as well as random topologies. VanetMobiSim provides a flexible platform in which the user can configure the shortest path during a trip based on the criteria of Dijkstra, road speed and road density. The trip can either be generated by random source-destination or according to activities [15].
## 2 Dual-User Operation of Remote Robot System with Force Feedback
### Experiment Method for Clarifying Mechanisms  
We employ a dual-user operation system to collaborate with a remote robot system to do cooperative work (see Figure 4) involving the movement of an object (a wooden stick) clamped by the two robot arms in the front-back direction, as in [23]. In this work, after moving the wooden stick from the initial position to the paper block (see Figure 5) at the front side, the wooden stick is moved backward to the paper block at the back side. The distance between the initial position and the paper block positioned at the front is approximately 40 mm and takes about 5 s, while the distance between the front paper block and the back paper block is about 80 mm and takes about 10 s. Since it is difficult to identify influencing factors if the robot arm (referred to as robot arm 1) of the remote robot system is also controlled by a human, robot arm 1 moves automatically at a constant speed of about 8 mm/s, ensuring consistent movement in each operation. To move robot arm 1 automatically, we calculate a new position based on the current position and movement speed (i.e., 8 mm/s) approximately every 3.5 ms, which is the position update cycle for the robot arm. The robot then updates its position based on the calculation result. The other robot arm (_robot arm 2_) is controlled by two users to move in the same manner as robot arm 1. The robot position control is implemented at the slave terminal of robot arm 2 to determine the position vector for robot arm 2.  
In the experimental system, we use static IP addresses for each PC, and since the processing latency of each PC is only a few milliseconds, which is much shorter than the network delay, we ignore the processing latency in our experiment. A constant network delay is generated for packets transmitted between master terminals 1 and 2 and the slave terminal of robot arm 2 by a network emulator (NIST Net [28]). For one user, the constant delay ranges from 0 ms to 250 ms in 50 ms increments, whereas for the other user, the delay ranges from 0 ms to 600 ms in increments of either 50 ms or 100 ms.  
The work was repeated 20 times for each set of network delay combinations. We measured the force of robot arm 2 approximately every 3.5 ms. Then, we averaged the force magnitude based on the output data and obtained the maximum force magnitude. Finally, we calculated the average force magnitude and the average maximum force magnitude over the course of the 20 trials.
## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
These ML models served as wrappers during feature selection and for comparative evaluation of the metaheuristic algorithms. Table 4 presents the detailed parameter settings for the ML models. This systematic experimental setup ensured a comprehensive evaluation of the feature selection and regression models, setting the foundation for the comparative analysis in subsequent sections. Comparative Performance Analysis of Proposed Models with Metaheuristic Algorithms and Regression Models

This section evaluates the proposed methodology for predicting BGLs through a comprehensive analysis of the combinations of metaheuristic algorithms and regression models. The evaluation was conducted in two phases to systematically assess and refine the models. Phase One focused on evaluating all metaheuristic-regression combinations through 30 independent runs to ensure robust comparisons, while Phase Two refined the best-performing combinations using 10 independent runs to optimize accuracy, feature sets, and computational efficiency further. \begin{table}
\begin{tabular}{c c} \hline \hline
**Regression Model** & **Selected Parameters** \\ \hline RF & random\_state = 162, n\_estimators = 60, max\_depth = 7, \\  & min\_samples\_split = 3 \\  & random\_state = 7, ccp\_alpha = 12, n\_estimators = 70, \\  & criterion =’squared\_error’, max\_depth = 19, \\  & min\_impurity\_decrease = 0.1 \\  & num\_leaves = 31, verbosity = -1, learning\_rate = 0.01, \\  & n\_estimators = 500, max\_depth = 5, min\_child\_samples = 10, \\  & reg\_alpha = 0.25, reg\_lambda = 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings of regression models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metaheuristic Algorithm** & **Selected Parameters** \\ \hline IDA & n\_iteration = 50, population\_size = 50, method = ’sinusoidal’, \\  & beta = \(\pi\)/2 \\ BHHO & n\_iteration = 50, population\_size = 50, beta = 0.5 \\ bGWO & n\_iteration = 50, population\_size = 50, method = 2 (sigmoidal \\  & function) \\ GA & n\_iteration = 50, population\_size = 50, selective\_pressure = 2, \\  & elitism = 2, mutation\_rate = 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings of metaheuristic algorithms. In terms of selected features, MAE, RMSE, CEG Zone A percentage, and time costs, the results of the models were comprehensively analyzed with mean, standard deviation, and the best values across multiple runs to capture their clinical relevance. Although computational time was documented as a measure of efficiency, it is not a primary criterion in this study. #### 5.4.1 Phase One: Initial Evaluation of Model-MH Combinations

The results of the 12 combinations of metaheuristic algorithms and regression models are summarized in Table 5. Among the algorithms tested, the IDA consistently demonstrated superior performance across all ML models. IDA stood out for its ability to balance feature selection with predictive accuracy, making it the most reliable and versatile choice for non-invasive BGL estimation.