esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Overview of FL Models
#### 2.1.2 Overview of the DFL Strategy  
In contrast to CFL, DFL, illustrated in Figure 0(b), eliminates the need for a central server to manage the learning process [32; 33]. Instead, each client exchanges model parameters directly with other clients to update its local model. The final model is obtained on an edge device by aggregating local updates from the connected edge clients. In the context of wireless systems, DFL is particularly well suited for wireless systems that utilize device-to-device (D2D) communications [34]. Like CFL, the primary goal of DFL is to optimize the global parameters \(\mathbf{q}\) by minimizing the global loss function in (1).  
The DFL process during the \(j\)-th iteration involves the following steps:  
Step 1 (Local computation): At the beginning of the DFL process (\(j=0\)), all \(K\) clients initialize their local models with the same structure. During the \(j\)-th iteration, the \(k\)-th client updates its local model, \(\bar{\mathbf{q}}_{k}^{(i)}\), using its dataset, \(\mathbf{D}_{k}\), and optimization algorithms such as SGD [35; 36].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristic** & **CFL** & **DFL** \\ \hline Architecture & Central server aggregates updates from devices & Peer-to-peer communication without a central server \\ \hline Communication Overhead & High, as all devices communicate with the server & Lower, as communication is localized among peers \\ \hline Scalability & Limited by central server capacity & High, as no central bottleneck exists \\ \hline Fault Tolerance & Vulnerable if the central server fails & More resilient, as no single point of failure exists \\ \hline Energy Efficiency & May require more energy due to server-device interactions & Typically more energy-efficient due to localized operations \\ \hline Privacy & Relies on the server’s privacy measures & Offers better privacy as data exchanges are localized \\ \hline Use Case Suitability & Suitable for robust infrastructure & scenarios with Ideal for distributed systems with limited infrastructure \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of CFL and DFLStep 2 (Model aggregation via D2D communications): After completing the local computation, each client shares its trained model with neighboring clients over the wireless links. Using the FedAvg method in [31], the \(k\)-th client updates its local model by aggregating the models received from other clients:  
\[\mathbf{q}_{k}^{(i)}:=\sum_{k^{\prime}=1}^{K}\alpha_{k,k^{\prime}}\beta_{k,k^{ \prime}}\mathbf{q}_{k}^{(i)}, \tag{3}\]  
where \(\beta_{k,k^{\prime}}\) is the aggregation weight between clients \(k\) and \(k^{\prime}\), with values ranging from 0 to 1 [32], and \(\alpha_{k^{\prime},k}=\{0,1\}\) indicates the presence or absence of a D2D link between the two clients. The DFL process iterates until a global consensus is reached, yielding an optimized model.
## 1 Introduction
### Homomorphic Encryption  
VICINITY's architecture offers privacy built-in by design, as only metadata on the connected devices is stored in a central cloud. Sensitive information, e.g., sensor readings, are transmitted peer-to-peer only from the data producer to its intended consumer. Additionally, this needs to be approved by the data owner in advance and on an individual basis. However, once the data owner has given his consent, his data are given out and will be available to potentially malicious third parties, which may seem trustworthy at first glance. As Value-Added Services (VAS) are the key to making the whole IoT \"smart\", a user may be tempted to share his data with such a service, even though he would rather not give out his information. An even better approach would be to never give away any sensitive data (in clear text), yet still allowing the VAS to work. What may sound contradicting is exactly what is possible with the use of homomorphic encryption (HE) Schemes. Homomorphic encryption enables certain calculations, or, in the case of fully homomorphic encryption, any arbitrary function to be executed on encrypted ciphertexts without the need to decrypt this data first. Partially homomorphic encryption has been subject to research for quite some time, only enabling a limited  
Figure 1: High-level VICINITY architecture [4].  
number of operations to be executed. With the introduction of a fully homomorphic encryption Scheme by [PERSON] in 2009 [5; 6], any arbitrary computation is now possible on ciphertexts with no need to decrypt and giving out any cleartext information at all. However, fully homomorphic encryption is more expensive in terms of computational demand, so people need to decide on a trade-off between performance and versatility. As we demonstrate in Section 5.5, using our Framework enables us to evaluate different options in a realistic but controlled environment.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 5 ECM-Derived Peptide Sequences and Their Use in Biomimetic Hydrogel Functionalisation
### Fibronectin: LDV Motif  
LDV is a fibronectin-derived tri-amino acid sequence, capable of promoting cell adhesion in numerous cell types, such as lymphocytes [112]. This specific adhesion site is found in the connecting segment 1(CS1) peptide within the Heparin II/III connecting segment (Hep II/IIICS) region, in which the III/connecting segment domain (III/CS domain) or variable region (V region) acts as an \(\alpha\)4\(\beta\)1 recognition site [112], specifically between residues 2102-2104 and 2466-2468. LDV-containing sequences are considered to be one of the most significant cell adhesion sites found in fibronectin, alongside the RGD sequence [113].  
The first incorporation of the tripeptide motif LDV within a supramolecular peptide hydrogel was reported by [PERSON] et al. [114], where hydrogel composition included the self-assembling tripeptide FFL, a key initiator of supramolecular hydrogel formation, along with the bioactive motif LDV [114]. The formed hydrogel with and without the presence of LDV showed evidence of high fibroblast viability; however, cell spreading within the hydrogel was only observed in the presence of LDV [114]. Through the addition of manganese (Mn\({}^{2+}\)), a known co-factor for integrin binding, the cell adhesion response towards LDV increased, implying that the LDV motif was capable of successfully mimicking the ECM [114]. The addition of an integrin-\(\beta\)1-blocking antibody conversely hindered cell adhesion, further confirming the relationship between integrins and the LDV motif as a driver of cell adhesion. Overall, this peptide scaffold with the addition of an LDV motif can be seen as a biomimetic hydrogel capable of cell adhesion, spreading and viability through activation of \(\beta\)1 integrin proteins [114].
## 3 Experiments and Results
### Experimental Results and Analysis  
The experimental design centers on three core axes: model performance validation, component-wise effectiveness analysis, and cross-dataset generalization. First, comparative experiments assess the detection accuracy and computational efficiency of Orga-Date relative to state-of-the-art models. Second, ablation studies quantify the individual contributionsof key components--such as attention mechanisms and loss function strategies--to overall detection performance. Finally, generalization capability is evaluated across multiple organoid datasets, with comparisons drawn against baseline methods from existing literature.
## 4 Simulations  
As we previously discussed in Section 2.2, in field measurements [67] there were situations when requests for data with possibly higher priority occurred. The key question was how those streams are going to be transferred through the bottlenecks of linear network topology.  
As a part of our research in PLC networks, three types of simulations were conducted. In the first, the behavior of multiple streams on one shared medium was studied. In the second, balancing time of two streams was studied with one and two repeaters. In the third, previously obtained measurement results were used as input values for determining the effect of repeaters on data throughput. Multiple repeater scenarios are investigated with network bottlenecks alongside the route.
## 2 Materials and Methods
### Description of the Data Set and Study Area  
This paper uses SCADA data from the VWF, located in Ecuador, between UTM coordinates 693,030 E 9,558,392 N and 693,526 E 9,556,476 N. The VWF is located in a mountainous area at approximately 2720 m.a.s.l., and includes 11 GOLDWIND GW70/1500 WTs of 1.5 MW nominal power with Direct Drive technology [10; 15]. Based on the available information, there are few wind farms located at that altitude or higher.  
The SCADA operational data and fault records correspond to WT2 of the VWF, recorded between 1 January 2014, and 31 October 2021, representing 386,288 records and 69 variables.
## 2 Materials and Methods  
### Experimental Setup  
Figure 1 shows a schematic of the experimental setup for the EWS; the cylindrical absorption column had a diameter of 80 mm and a height of 250 mm, corresponding to an effective volume of 1 dm\({}^{3}\). The rectangular electrochemical oxidation reactor had a size of 150 (width) \(\times\) 90 (length) \(\times\) 155 mm (height), and an effective volume of 1 dm\({}^{3}\). An NaCl solution (10 mM) was continuously circulated throughout the reactor at a flow rate of 1 dm\({}^{3}\) min\({}^{-1}\) (circulation rate: 30 rev. h\({}^{-1}\)). The solution was introduced from the bottom of the absorption column into the electrochemical oxidation reactor, using a peristaltic pump, and the aqueous solution in the reactor overflowed into the absorption column. The pH of the solution was maintained at 8, which was automatically controlled with a pH controller (SOWA 910, Istek, South Korea) using 0.1 M HCl and NaOH aqueous solutions. During the experiment, the injected solutions for controlling the pH represented less than 10% of the entire volume. A thermostat was installed, and the temperature of the aqueous solution was maintained at 25 \({}^{\circ}\)C to prevent temperature variations during the experiment that would affect the chemical reactions.  
The model gas was prepared by diluting H\({}_{2}\)S (5 vol. %) and NH\({}_{3}\) (5 vol. %) with nitrogen (99.9 vol. %), which was introduced into the reactor by adjusting the inlet flow rate, using a flow meter. The mixed gas passed through the aqueous solution as bubbles via a sparger installed at the bottom of the absorption column. The disc-shaped bubble sparger (Namkyung Tech, South Korea) was composed of a porous ceramic material with a diameter of 60 mm.  
Figure 1: Schematic of the experimental setup (G.S.P is gas sampling port; L.S.P is liquid sampling port; full line indicates gas stream; dotted line indicates liquid stream).
## 3 Seismic Attributes and Application
### Directional Edge Detection  
In general, faults possess various directions (azimuth). However, a field or basin has few major directions depending on the geo-mechanical settings caused by their tectonic regime. [PERSON] et al. [35] proposed a technique for directional edge detection that takes into consideration of the major direction of the fault using amplitude contrast methods.  
Amplitude contrast is a Sobel-based attribute computation of the amplitude derivatives between neighboring seismic traces, where nondiagonal neighbors are assigned double weights. The calculated differences were normalized, and the final value was calculated using three gradient calculations, where \(\mathrm{Sx}\), \(\mathrm{Sy}\), and \(\mathrm{Sz}\) are the weighting operators  
Figure 2: Workflow proposed in this study for fault detection consists of directional edge detection, summation, and edge enhancement. The workflow is significantly simplified, and the computational time is also reduced due to the application of reduced attributes.  
in the corresponding dimensions. Figure 3 shows the amplitude contrast cubes when the azimuth direction increases from \(0^{\circ}\) to \(170^{\circ}\) in steps of \(10^{\circ}\). It is evinced from the figure that the clarity of discontinuity varies with the azimuth.  
After calculating all directions, the cubes that display good continuity for faults were selected, and then the selected cubes were summed. Owing to this summation step, we can expect noise reduction of the background and enhancement of the edge (or fault) signal. This step is similar to the stacking process of seismic processing. The results of the directional edge detection and summation using the amplitude contrast are shown in Figure 4. It is evinced that, even with simple amplitude contrast attributes, the discontinuity of the formation is more prominent than that of the original cube section. Furthermore, it was observed that noise reduction after the application of both directional edge detection and summation improves the connectivity between discontinuities.
# XClinic Sensors: Validating Accuracy in Measuring Range of Motion Across Trauma Conditions
## 2 Methods
### Evaluation Using Sensors  
The XClinic sensors used in this study are wearable inertial measurement units (IMUs) developed by Ferrox. Each device includes a triaxial accelerometer, a gyroscope, and a magnetometer, allowing it to capture three-dimensional movement with good precision. The sensors operate with a fixed sampling frequency of 100 Hz and use a sensor fusion algorithm to calculate the joint angles in real time, expressed as Euler angles. Data are transmitted wirelessly to a tablet through a dedicated app, which displays and stores the information. The system is designed to be user-friendly and suitable for clinical use, and its measurement range covers the typical joint movements observed in rehabilitation settings. These features make the sensors a reliable and practical option for assessing ROM outside laboratory environments [18]. The XClinic kit included two wearable sensors, a charging device, adjustable straps and bands to secure the sensors to the body, instructional materials, and a tablet with the XClinic app installed, which served as the user interface. Before each assessment, the researchers carefully unpacked the sensors and related equipment and verified the presence of user manuals or quick-start guides. The sensors were then powered and charged to ensure that they were ready for use. Following the manufacturer's instructions, the devices were connected to a local Wi-Fi network to enable data transmission. To accurately capture joint movements, the sensors were placed on the upper and lower segments of the joint under examination, ensuring that the positioning aligned with the guidelines provided by Ferrox. The software was then used to configure and calibrate the sensors to guarantee precise measurements. For each participant, a new user account was created within the app, allowing individualized data tracking. Once all steps were completed, the assessment of the range of motion (ROM) began, with the software recording the data in real time (\"Riabilitazione digitale per centri di fisoterapia-Ferrox\", n.d.). The joint angle estimation is based on XClinic's proprietary algorithm, which uses a sensor fusion process to integrate data from the accelerometer, gyroscope, and magnetometer. This built-in algorithm calculates the angular displacement in real time and provides direct measurements of the joint range of motion. The algorithm has been previously validated in a healthy population and showed good agreement with goniometric measurements [18]. Figures 2-5 show the positioning of the sensors for the assessment of the shoulder, hip, knee, and ankle range of motion. For each joint, the sensors were placed as follows: on the thorax and wrist for the shoulder, on the thorax and thigh for the hip, on the thigh and lower leg for the knee, and on the lower leg and foot for the ankle.
## 3 Background Noise Modeling  
A model signal corresponding to the performance of an abstract rotary equipment is chosen to simulate background noise. We consider two types of acoustic pulses (Figures 6 and 7), recorded during the performance of real industrial rotary equipment. Type 1 pulse (Figure 6) corresponds to a local acoustic event, and Type 2 pulse (Figure 7)--to a continuous noise-like signal.  
Let us consider three signals that simulate the performance of the background equipment under different conditions. To do this, we construct background signals by scattering two types of acoustic pulses in a time interval of 0.5 s with a lognormal distribution of the intervals between them (\(\mu\)-1 ms, \(\sigma\)-0.5 ms). Figure 8a shows the background signal (the first signal) which corresponds to the single arrival of Type 1 pulses with random intervals. Figure 8b presents the second signal corresponding to the arrival of Type 2 signals with random intervals. A combination of both signals (third signal) is given in Figure 8c.  
Figure 8: _Cont._  
Figure 6: Typical shape of a single.  
Figure 7: Typical shape of a noise signal.  
For further development of the algorithm, the data corresponding to the four levels of intensity (maximum amplitude) has been constructed for each type of the background signal. The maximum signal amplitude ranges from 130 \(\mu\)V (minimum intensity) to 10 mV (maximum intensity).
# Integrated Design of Hydraulic Coupling Bolts for Large Shaft Systems
## 2 Calculation of Tightening Load
### FE Analysis for Tightening Process
Subsequently, the stress concentration factor (\(K_{\text{t}}\)) was calculated using Equation (3) based on this result. Figure 7b displays the variations in the mean contact pressure and maximum von-Mises stress on the sleeve, as the first tightening load changes while keeping the second tightening load constant. The x-axis in the graph represents the first tightening load for the quarter model. As the first tightening load increased, the mean contact pressure also increased due to an increase in the interference fit. However, after the second tightening, the contact pressure decreased in comparison to the first tightening since the bolt extended axially and reduced in diameter. Within the allowable stress limit defined by the \(SF\), the first tightening load was 452 kN (113 kN in a quarter model), which ensures the contact pressure between the sleeve and the coupling after the second tightening. This value is approximately equivalent to 400 bar as the tensioner hydraulic pressure. Figure 7c illustrates the contact pressure distribution on the sleeve after the entire tightening process under the load conditions, as per the design results, while Figure 7d shows the von-Mises stress distribution on the sleeve. It was observed that the clamping load in the radial direction was secured, as evidenced by the contact pressure generated across the entire contact surface between the sleeve and the coupling after the second tightening process. Meanwhile, the contact pressure decreased as the thickness of the sleeve increased because the expansion of the outer surface of the sleeve was relatively reduced. In addition, the analysis of stress distribution on the inner surface of the sleeve in contact with the bolt confirms that the structural integrity of the sleeve can be maintained after the tightening process. Figure 6: FE analysis conditions for the tightening process: (**a**) 3D FE model; (**b**) force distribution at each step; (**c**) boundary conditions. Figure 7: FE analysis results for tightening process: (**a**) von-Mises stress distribution on the bolt after the 2 nd tightening for stress concentration factor calculation; (**b**) mean contact pressure and maximum von-Mises stress variation on the sleeve according to the 1 st tightening load; (**c**) contact pressure distribution and (**d**) von-Mises stress distribution on the sleeve after the 2 nd tightening.
## 1 Introduction  
Hydrocarbon-containing wastewater is generated in large volumes in petroleum, metallurgical and transport industries, as well as in other water-intensive industries, agriculture and urban management. Oil production and refining processes consume large amounts of water and discharge hydrocarbon-contaminated wastewater streams, which are the main source of organic pollution of the environment. Petrochemical and metallurgical plants also produce complex wastewater streams, the volume and composition of which varies greatly depending on the raw materials and technologies used. Majority of the industrial wastes are hazardous as they contain toxic, mutagenic and carcinogenic substances. Consequently, industrial waste effluents typically require treatment before reuse or discharge to open water bodies. Stringent regulations on wastewater discharge are becoming stricter worldwide, with restrictions applied not only to industrial users but also to municipal wastewater treatment operations. Industry faces the challenge of reducing the wastewater it generates and attaining sustainable standards of operation. Water reuse and recycling define final concentrations of pollutants in wastewater and offer substantial potential for savings, for example, in petroleum refining [1]. Therefore, environmental legislation, on the one hand, and the needs of industry on the other, is the driving force in the development of advanced wastewater treatment facilities that provide high treatment efficiency at low capital, operational and maintenance costs.  
Urban transport and its services (repair shops, car washes, paint shops and filling stations) are a growing source of hydrocarbon-containing wastewater. Urban runoffs receive hydrocarbons mainly from transport but also from small industrial, commercial and domestic activities. Municipal wastewaters contain thousands organic compounds, among which petroleum hydrocarbons predominate. Also, emerging micropollutants (e.g., pharmaceuticals, personal care compounds, flame-retardants, biocides and pesticides) are increasingly detected in urban wastewaters [2].  
Industrial and municipal effluents are treated using conventional methods in activated sludge and aerated lagoon systems. These processes suffer from serious operational problems, while novel techniques, for example, membrane bioreactors are applied. Treatment of oily wastewater usually has two stages, firstly, pre-treatment to reduce oil, grease and suspended materials. Secondly, an advanced treatment stage to degrade and decrease the pollutants to acceptable discharge values. The complexity of the wastewater composition and stringent discharge limits require combinations of several treatment methods. There are several reviews published recently on the treatment of petrochemical wastewater [3; 4; 5] including biological methods [6; 7]. This review focuses on advanced bioreactor technologies, including hybrid and integrated physico-chemical and biological systems.