esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detailed Implementation of Layer-1 BoD Services
### Resource Management for Layer-1 BoD services  
We have to carry layer-2/3 service traffic whose volume has a very analogous daily pattern on each link in our network. To do so, we vary the available bandwidth for the layer-1 BoD services in each link according to the traffic volume of the layer-2/3 services (Figure 6). The available bandwidth is set to a larger value from 22:00 to 8:00 during weeknights and all hours on weekends. As the available bandwidth will likely be different from what is actually assigned, we have two more parameters in each link: the default bandwidth and the assigned bandwidth. The default bandwidth is the requisite minimum bandwidth for the layer-1 BoD services, and the assigned bandwidth is usually set to the default bandwidth. When the bandwidth requested by users for a link exceeds the default bandwidth of the link, the BoD server changes the assigned bandwidth of the link. The assigned bandwidth is set to the maximum requested bandwidth during a predefined period, such as from 8:00 to 22:00 during weekdays, from 22:00 to 8:00 during weeknights, or all hours on weekends. Here, we use the LCAS functions only at the start and finishing times of the predefined periods, because they require a few minutes to operate, as we describe later.  
Our network operators set the default and available bandwidths on a simple Web screen (Figure 7). As the network operators usually care about the traffic volume of the layer-2/3 services, the screen shows the bandwidths for those services. The default/available bandwidth for them is equal to the link  
Figure 5: Interface between layer-1 BoD server and L1-OPS. (a) For path setup/release. (b) For path bandwidth change.  
bandwidth minus the default/available bandwidth for layer-1 services. We use the granularity of 150 Mbps, which is indicated as \"v\" in Figure 7, and the operators set values of from 0v to 64v for each link. We divide the bandwidth of a 40-Gbps (STM-256) link into four bandwidth groups to accommodate four parallel virtual links established between the IP routers, and operators use the same value range for each bandwidth group.  
If the requested bandwidth exceeds the available bandwidth of a link, the layer-1 BoD server will try to rearrange the reserved layer-1 paths so that they can be accommodated on other routes. In attempting to fulfill user requests, the BoD server informs the network operators about any resource shortages. It sends the information about the resource in contention, such as the link ID, bandwidth, and duration, and recommends (_i.e._ automatically selects) contention resolution. The operators either accept the recommendation with/without minor changes or negotiate a resolution, such as a bandwidth reduction and duration reduction, with users.  
Figure 6: Default/available/assigned bandwidth for layer-1 services.  
Figure 7: Screen example: bandwidth management of layer-2/3 services.
# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 3 Methodology
### Improved Network  
Although YOLOv11 has achieved significant improvements in image object detection tasks, it still lacks targeted optimization methods to address the characteristics of sonar image targets, such as feature scarcity and severe noise interference. Direct application of the original network for sonar image target detection often suffers from high false alarm rates and low accuracy.  
To address these challenges, we propose SFE-YOLO based on the YOLOv11n baseline model. In SFE-YOLO, we innovatively integrate ASHF (Adaptive Spatial Feature Fusion) into the detection head and introduce an additional P2 small-object detection head to effectively preserve semantic information of tiny targets. Furthermore, the proposed Edge-Contour Attention Mechanism (EEA) incorporates a dual-branch gradient guidance architecture, which significantly enhances gradient response characteristics at target edges through differentiated gradient information processing. This mechanism achieves adaptive focusing on critical contour regions while effectively suppressing boundary reverberation noise interference. Additionally, we synergistically combine the structurally reorganized and optimized SC_Conv module with C3K2 to form a feature extraction unit with a dual-path compression mechanism, enabling high-fidelity feature representation while reducing information redundancy. Finally, Focaler-IOU is introduced to replace the original loss function, addressing the adaptability shortcomings of existing bounding box regression methods for diverse objects. The overall structure is shown in Figure 4.
## 2 Background
### Internet of Things (IoT)  
The Internet of Things, or IoT, allows physical objects to be identified and tracked through the collection of relevant data, which will be transformed into useful information and transmitted over a network, enabling the creation of alerts for situations that may require some type of correction. This concept provides agility and visibility in tracking and sharing information, facilitating the planning and in the control of processes. Thus, an object capable of establishing connection through the Internet and that communicates with other objects in a network is considered a \"thing\". Its application can improve efficiency in several fields, such as, households appliances, electronics, furniture, agriculture, industrial machinery [10; 11]. In general, IoT architecture is composed of four layers, considering the main components of IoT solutions: perception, transport, processing and applications.  
* Perception/smart device: this layer enables to connect physical devices in a digital network, allowing real-time information to be collected and processed. Sensors can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & \begin{tabular}{c} **Total Output** \\ **(Million EUR)** \\ \end{tabular} & \
\begin{tabular}{c} **Crop Output** \\ **(Million EUR)** \\ \end{tabular} &
\begin{tabular}{c} **Animal Output** \\ **(Million EUR)** \\ \end{tabular} \\ \hline Portugal & 6796 (2.0\%) & 4157 (2.1\%) & 2639 (1.8\%) \\ EU 27 (100\%) & 341,098 & 196,082 & 145,015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Portugal and EU: comparative total agro-industry output, crop output and animal output, in 2019 [7].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Subject of Papers** & **Identified Papers** \\ \hline Application of IoT in agro-industry & 33 \\ Smart farming, intelligent farming or precision agriculture & 14 \\ Monitoring, measuring, controlling and tracking & 6 \\ Artificial Intelligence, data driven decisions & 5 \\ Block chain & 2 \\ (Cybersecurity & 2 \\ Big data & 5 \\ Robotics & 2 \\ Unmanned aerial vehicles (UAV) & 2 \\ Opportunities and barriers for IoT & 5 \\ Others & 3 \\ \hline \hline \end{tabular} Note: The papers described above can be related to one or more categories.  
\end{table}
Table 3: Main results of IoT applied in agro-industry.  
perform measurements of temperature, air quality, speed, humidity, pressure, flow, movement, electricity, etc.
* Transport: after measurements are done, the results must be transmitted. This layer enables Internet connection through networks and protocols, such as WiFi, global system for mobile communications (GSM), Bluetooth, etc.
* Processing: the collected and transmitted information becomes data and needs to be stored, filtered, processed, and analyzed.
* Application: this is the interface layer, allowing monitoring, decision-making, controlling, regulating, command, etc. Concepts are changed when, for example, they are applied to agriculture, turning a simple a farm into a smart farm.  
Some other authors refer to IoT with three layers [12] and others with five layers [13].
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 6 User Study 2: Teleportation and Sorting
### Results
#### 6.4.1 Task Completion Time  
An ANOVA failed to identify a significant effect of the device on the teleportation task's completion time (\(F_{2,22}=1.10\), \(p=0.35\)). On average, the mouse achieved the fastest task completion time at 27,410 ms for teleportation, followed by the Digital Thimble at 29,137 ms and the controller at 29,880 ms. But an ANOVA did identify a significant effect of the device on the sorting task's completion time (\(F_{2,22}=3.61\), \(p<0.05\), \(\eta^{2}=0.04\)). The mouse achieved the fastest task completion time in the sorting task at 13,040 ms, followed by the controller at 15,834 ms and the Digital Thimble at 16,056 ms. A Duncan test indicated that the mouse was significantly faster than the other two devices in sorting tasks, while there was no significant difference between the controller and the Digital Thimble. Figure 17 presents the average task completion time, categorized by task and input device.
###### Abstract  
The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare.  
decentralized identity management; IOTA Tangle; Internet of Things (IoT); Web 3.0; security +
Footnote †: journal: Future Internet  
_Article_
# Review of Ka-Band Power Amplifier
###### Abstract
The structure of the state-of-the-art PA is presented, including common source, cascode, stacked FET, power combining, and Doherty PA, highlighting the advantages, disadvantages and a comparison with them. The whole paper is arranged as follows: Section 2 briefly introduces the technology which can be used in Ka-band power amplifiers. Section 3 describes the topologies of the common PA and summarizes its advantages and limitations. Finally, Section 4 summarizes the conclusion of this paper. Figure 1: A block diagram of a communication system transmitter.
# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
## 2 Related Work  
### Blockchain-Based Federated Learning  
In recent years, the study of BFL frameworks has seen remarkable growth, drawing extensive research interest due to their auditable training processes and serverless architectures, which avoid the single point of failure typical in FL systems. Traditional BFL methods replace the original server with a blockchain, increasing generality and security. For instance, the paper [16] introduces a multi-layer BFL framework, where the upper layer is responsible for global model management, and the lower layer focuses on resource scheduling and updating local models. By utilizing a blockchain, a blended framework of a blockchain and FL has been proposed to manage security and trust issues when applying FL in mobile edge networks. Furthermore, [PERSON]. [12] employs a top-k model compression mechanism in the centralized BFL framework to improve the overall performance, while [14] introduces an autonomous BFL design for privacy-aware and efficient vehicular communication networks that theoretically optimizes Proof of Work (PoW), reducing the latency of BFL.  
In research focusing on blockchain mechanisms in BFL, particularly regarding security against malicious attacks on global models or user privacy data, BPLC [15] designs committee mechanisms and block structures to assist in model storage and downloading and innovatively reduces the computation required for consensus and the potential for malicious attacks. FGFL [6] calculates client trustworthiness within the blockchain committee for election, determines aggregation weights based on client contributions, and enhances the system's convergence and effectiveness. In terms of reward mechanisms, FedTwin [8] relies on a centralized BFL framework and utilizes a Generative Adversarial Network (GAN) to ensure privacy preservation, establishing a reward mechanism based on model performance. Additionally, PF-PoFL [9] addresses the drawbacks of PoW and proposes a novel energy-recycling consensus mechanism that utilizes computational power wasted on difficult but meaningless PoW puzzles for practical FL tasks.  
The above introduces various examples of blockchain applications in FL. We also apply the blockchain to FL scenarios that we need to improve efficiency and security. Current BFL frameworks primarily focus on optimization and improvement within FL or the blockchain separately. Our pFedBASC emphasizes the collaborative design of both the blockchain and FL. We will introduce a method to support semi-centralized FL training throughout the entire process starting from the blockchain design.
## 3 Result and Discussion
### Three-phase system (Hybrids)  
The calculation of the percolation of hybrid composite requires the modification of composite generation procedure Section 2.3. The generation is organised \"one in time\". We introduce the parameter _prob_--the probability of the GNP appearance, then 1-_prob_ is the probability of the CNT appearance. So, upon the generation procedure, each \(i\)-th particle has the probability to be \"born\" as GNP or CNT. After, the percolation was computed using the standard protocol. For the hybrid  
Figure 4: The empiric percolation probability function for \(\rho=5\) ellipsoids inside cells of different sizes (symbols); Weibull probability distribution functions(solid curves).  
composite, we consider \(p_{c}\) as a total volume fraction of the inclusions required for the percolation. Obviously, for the hybrids total concentration may be presented as a sum of partial concentrations as \(p_{c}^{tot}=p_{c}^{CNP}+p_{c}^{CNT}\). The dependence of the \(p_{c}^{tot}(prob)\) is presented on Figure 6. For the case of prob = 1 and 0, the data was verified with the model for 1 type of inclusions. For the calculations two types of ellipsoids were used: \(b_{1}^{1}=b_{2}^{1}=20\) nm, \(b_{3}^{1}=2\) nm, and \(b_{1}^{2}=b_{2}^{2}=2\) nm, \(b_{3}^{2}=20\) nm  
The excluded volume theory states that the percolation for the hybrid composites [40] occurs if the following condition is satisfied:  
\[EVT=\frac{p_{c}^{CNP}}{p_{c}^{tot}(1)}+\frac{p_{c}^{CNT}}{p_{c}^{tot}(0)}=1, \tag{6}\]  
Figure 5: Dependence of the percolation concentration on the aspect ratio of the ellipsoid ( \(b_{3}\) = 2.38 and \(n=3\)). Experimentally observed percolation concentrations: \(\triangle\)—[36]; \(\bigcirc\)—[37] ; \(\lozenge\)—[38].  
Figure 6: Dependence of the percolation concentration for hybrid composites of different composition.  
where \(p_{c}^{GNP}\) and \(p_{c}^{CNT}\) are partial concentrations of the GNP and CNT in hybrid composite, and \(p_{c}^{tot}(1)\) and \(p_{c}^{tot}(1)\) are the critical concentrations for composites with GNP (\(prob=1\)) and CNT (\(prob=0\)). However, a lot of experimental works evident the percolation even if the condition Equation (6) is not satisfied [41; 42; 43]. In our case, the exact number of GNP and CNT is known for each configuration, so we can easily calculate the EVT-parameter. The result is presented in Figure 7. The mean value of EVT is below 1 for all studied configurations. The dependence demonstrates minimum at \(prob=0.7\)-0.8. We can preliminarily conclude that there is some optimum combination of CNT and GNP in hybrid composites.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### Generic VANET Architecture  
The two main communication models in VANETs are classified as Vehicle-to-Vehicle (V2V) communication and Vehicle to Infrastructure (V2I). V2V communication is the ability of exchanged transmission between different vehicles while V2I is the communication between the vehicles and the road-side framework. The main communication module consists of Road-Side Unit (RSU), on-board unit (OBU), and trusted authority (TA) as shown in Figure 1. The RSU unit is fixed and made up of a transceiver that sends and receives information from the OBU and TA.  
Therefore, it acts as a communication frontier between OBU and TA. RSUs can be placed at key indicators after particular interlude to provide reliable network coverage and systematic operation. Therefore, the distance between nearby road-side units is kept within the range of each sequential RSU. OBU is fixed in the automobile and works as a central point for receiving, processing and managing all data developed in the vehicle. It also serves as a source for exchanging data with the OBU of close by vehicles as well as the RSU. TA is basically the foundation of the whole ITS and is usually connected to the RSU via fiber optic cable or wireless media. It manages the trust and safety of VANETs. TA verifies the entire network components via RSU and identifies the OBU that sends malicious packets and cancels the target node. TAs is in the center of the town and is managed by governments. The VANET system model is briefly described in Figure 1 below. A more detailed survey on this can be found at [4].
## 3 Proposed Framework
### Services/Applications Layer  
This layer plays an important role in solving the traffic congestion problem as it can provide services such as pre-booking of car parking, supporting mass transportation, especially for students and employees, in addition to applications for medical emergencies, as well as applications that contribute to the sharing of information among users about  
Figure 1: General structure of the proposed framework.  
congestion in specific areas. Finally, various LBS applications, such as searching for points of interest or the shortest or fastest path, tracking, and other applications, can be realized in this layer. This layer enables service providers to add their services and their applications. The data generated by these applications and services will also serve as a source of new data in the cloud.
## 2 Results and Discussion
### Volatile Compounds Analysis
#### 2.2.2. Alcohols  
Alcohol is another important volatile compound in wine which is mainly produced through yeast metabolism during wine fermentation and could contribute pungent scents to wine aroma. The aromatic contribution of alcohol depends on its concentration in wines. They could provide a pleasant aroma when their concentration bellows at 300 mg/L, on the contrary, a pungent aroma would be perceived [23; 25]. A total of 14 alcohols were found in the experimental wine samples (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compounds** & **RI** & **C** & **BT** & **SP** & **CF** & **MF** \\  & **DB-WAX** & **mg/L \(\pm\) SD** & **mg/L \(\pm\) SD** & **mg/L \(\pm\) SD** & **mg/L \(\pm\) SD** & **mg/L \(\pm\) SD** \\ \hline Acids & & & & & & & \\ Acetic acid \({}^{*}\) & 1452 & 0.621 \(\pm\) 0.018 \({}^{a}\) & 0.593 \(\pm\) 0.013 \({}^{a}\) & 0.606 \(\pm\) 0.016 \({}^{a}\) & 0.603 \(\pm\) 0.078 \({}^{a}\) & 0.451 \(\pm\) 0.022 \({}^{b}\) \\ Isobutanoic acid & 1581 & 0.012 \(\pm\) 0.001 \({}^{a}\) & ND & 0.012 \(\pm\) 0.001 \({}^{a}\) & 0.011 \(\pm\) 0.001 \({}^{a}\) & 0.010 \(\pm\) 0.000 \({}^{b}\) \\
2-Methyl butan acid & 1655 & 0.065 \(\pm\) 0.001 \({}^{a}\) & 0.054 \(\pm\) 0.001 \({}^{a}\) & 0.052 \(\pm\) 0.001 \({}^{b}\) & 0.056 \(\pm\) 0.002 \({}^{a}\) & 0.052 \(\pm\) 0.002 \({}^{b}\) \\ Hexanoic acid & 1851 & 0.652 \(\pm\) 0.002 \({}^{a}\) & 0.617 \(\pm\) 0.024 \({}^{a}\) & 0.598 \(\pm\) 0.029 \({}^{b}\) & 0.640 \(\pm\) 0.024 \({}^{a}\) & 0.591 \(\pm\) 0.017 \({}^{c}\) \\ Heptoic acid & 1960 & 0.029 \(\pm\) 0.002 \({}^{a}\) & 0.027 \(\pm\) 0.001 \({}^{b}\) & 0.026 \(\pm\) 0.001 \({}^{b}\) & 0.028 \(\pm\) 0.002 \({}^{a}\) & 0.025 \(\pm\) 0.001 \({}^{c}\) \\ Octanoic acid & 2050 & 1.237 \(\pm\) 0.03 \({}^{a}\) & 1.091 \(\pm\) 0.086 \({}^{b}\) & 1.060 \(\pm\) 0.052 \({}^{b}\) & 1.122 \(\pm\) 0.034 \({}^{b}\) & 0.797 \(\pm\) 0.021 \({}^{c}\) \\ Nonanoic acid & 2169 & 0.066 \(\pm\) 0.001 \({}^{a}\) & 0.016 \(\pm\) 0.001 \({}^{c}\) & ND & 0.036 \(\pm\) 0.002 \({}^{a}\) & 0.025 \(\pm\) 0.001 \({}^{b}\) \\ Decanoic acid & 2279 & 0.757 \(\pm\) 0.011 \({}^{a}\) & 0.596 \(\pm\) 0.016 \({}^{b}\) & 0.506 \(\pm\) 0.019 \({}^{c}\) & 0.745 \(\pm\) 0.013 \({}^{a}\) & 0.109 \(\pm\) 0.006 \({}^{d}\) \\ Dodecanoic acid & 2502 & 0.072 \(\pm\) 0.001 \({}^{a}\) & ND & 0.035 \(\pm\) 0.003 \({}^{c}\) & 0.069 \(\pm\) 0.002 \({}^{a}\) & 0.064 \(\pm\) 0.001 \({}^{b}\) \\ Subtotal & 3.399 \(\pm\) 0.049 \({}^{a}\) & 2.994 \(\pm\) 0.107 \({}^{c}\) & 2.860 \(\pm\) 0.068 \({}^{c}\) & 3.241 \(\pm\) 0.074 \({}^{b}\)  
There was no significant difference between control wine and SP fining wine samples in total alcohol content, however, other clarification treatments showed a significant decrease, especially the wine treated by the MF. There was no obvious influence between BT and CF treated wines. Pentanol, 1-hexanol, and phenethyl alcohol showed relatively higher concentration among alcohols, however, unlike pentanol, the concentration of 1-hexanol and phenethyl alcohol only showed a significant decrease in MF treated wines.