esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Linear and Spiral Hashing Algorithms
## 2 Background
### Spiral Hashing  
A drawback of LH is the discrepancy between buckets that are already split and those that are not yet split at the same time. The former buckets tend to have twice as many records as the latter. As we will see in Section 3, the number of records rehashed when a file is split depends on the split pointer and varies between \(B/2\) and \(B\), where \(B\) is the maximum expected number of records in a yet-to-be-split bucket. To avoid this type of behavior, Spiral Hashing (SH) was invented by [PERSON] [4; 5; 7]. SH intentionally distributes the records unevenly into Buckets \(s\), \(s+1\), \(s+2\), \(\ldots\), \(2s-1\), where \(s\) is the file state, as shown in Figure 2. If desired, a simple address translation keeps the buckets in the range 1, 2, \(\ldots\), \(s\). We will not be using this final translation step here. When the file grows, the contents of Bucket \(s\) are distributed into the new buckets, Bucket \(2s\) and Bucket \(2s+1\). Afterward, Bucket \(s\) is deleted. If the hash used is completely uniform, then the probability that a record is allocated to Bucket \(i\) is \(p_{i}=\log_{2}(1+1/i)\). This remarkable feat is achieved with the logarithmic address calculation given in Figure 3. SH can be easily modified to generate \(d\) new buckets for each freed one. It is also possible to use an approximate address calculation. However, modern chip architectures with their efficient floating-point units obviate the need for speeding up the address calculation.  
We now reuse our previous example to show the evolution of an SH file in Figure 4. LH uses keys that are unsigned integers, but SH uses keys that are floating-point numbers in \([0,1[=\{x|0\leq x<1\}\). Therefore, we divide the keys by 100 so that key 40 is translated to key 0.40. Using the SH addressing algorithm in Figure 3, we obtain the picture in Figure 4 (top). Since \(s=5\), the range of buckets is from Bucket 5 to Bucket 9. We can see that the records inside a bucket are all in a sub-interval of \([0,1[\). Therefore, without calculation, we can guess that the record with key 12, translated to 0.12, is either in Bucket 8 or Bucket 9. After insertion of the record with key \(12\to 0.12\), the average load of the buckets is again higher than the threshold of 3, and Bucket 5 is replaced with Buckets 10 and 11. The mathematics of the address calculation algorithm again guarantees that no records outside of the split bucket are moved when we recalculate with the adjusted \(s\).  
Figure 2: Spiral Hashing with S = 7 before and after splitting.
## 2 Related Work
### Feature Aggregation  
Integrating feature information from different levels, sources, or temporal phases is crucial for capturing changes in remote sensing images. Existing change detection algorithms can be divided into early fusion, intermediate fusion, and late fusion according to the position of the fusion module [4]. Early fusion typically relies on a single-branch structure to directly perform feature fusion at the image level through operations such as differencing, concatenation, or summation [17]. Intermediate and late fusion generally act on a siamese network structure, covering single-scale fusion [12; 18; 19] and multi-scale fusion [20; 21; 22].  
Single-scale fusion typically focuses on fusing bi-temporal features of two siamese branches, while multi-scale siamese networks usually extract bi-temporal features from multiple levels and integrate multi-scale information through cross-scale fusion strategies. Given that change objects in remote sensing images often have irregular and multi-scale characteristics, the composite feature maps generated by multi-scale fusion overcome the limitation that covering multi-size targets is difficult with single-scale features, and cross-level feature complementarity strengthens the parsing ability of complex scenes. In addition, most existing multi-scale models have difficulty in modeling contextual relationships, which is crucial for identifying changes in interest in remote sensing images.
## 3 Results
### Biophysical Opportunities for Low ILUC Risk Crops
#### 3.1.2 Lignocellulosic Crops  
Lignocellulosic crops have been cultivated for more than two decades throughout Europe. They exhibit high yields, have specific traits for bioenergy and biofuel uses, and can grow in land with natural constraints that does not compete for food/feed crops. As such they are considered eligible as feedstock for biofuels.  
Willow can tolerate a great range of soils (e.g., wet sites, alkaline, saline, clay soils, etc.) and can remediate soil for land conservation practices, shelterbels, and windbreaks. It can prevent soil erosion [16; 26] and can also be used for biofiltration, constructed wetland, and wastewater treatment systems.  
Poplar can be grown as a short rotation woody crop and harvested every two to five years. Researchers are working to breed improved varieties of poplars for bioenergy, carbon sequestration, phytoremediation, and watershed protection through the development of poplar genotypes with improved yield, higher pest resistance, increased site adaptability, and easy vegetative propagation.  
Biomass sorghum is an annual herbaceous spring C4 crop that can be cultivated throughout most of Europe. Temperature, however, is a limiting factor, so it is more suitable to the Mediterranean zone.  
Tall wheatgrass is a tall, coarse, late-maturing bunchgrass. It is native to saline meadows and seashores, has high tolerance for drought, and has been cultivated in rainfed conditions. It is a perennial crop with a high regrowth capacity. It requires spring rainfall (April-June), however; if there is low precipitation in spring it will result in low biomass yields.  
Miscanthus can be grown across all Europe and is already cultivated at commercial scale in several countries. The crop is considered beneficial for the mitigation of soil erosion and allows high level of carbon storage in soil due to high levels of plant residue from above and below ground.  
Switchgrass can be grown successfully across Europe in different type of soils and ecological conditions, including land with natural constraints, because of its extensive root system [16; 17]. It is tolerant to drought and can retain high productivity under drought conditions.  
Giant reed is a common weed in the Mediterranean, and it is known to be invasive and out-compete other crops. It is drought tolerant and can also grow in saline, poor texture soil with steep slopes, as well as in contaminated lands for phytoremediation [16; 17; 27].  
Reed canary grass is a tall, perennial bunchgrass that commonly forms extensive single species stands along the margins of lakes and streams and in wet open areas, with a wide distribution in Europe.
# Local Phase Segregation Induced by Ion Milling in 2:17-Type Sm-Co Based Magnets
## 3 Results  
### Average Structure of the Final Magnet  
As shown in Figure 1, the powder XRD pattern was taken to assess the average structure of the final magnet. All the fundamental reflections of the 2:17R and 1:5H phases are overlapped with each other since these two phases are coherent with each other ([100]-2:17R // [210]-1:5H, (003)-2:17R // (001)-1:5H) and the difference between the lattice parameters of them are small (\(a/\sqrt{3}\)-2:17R \(\approx\)\(a\)-1:5H, \(c/\)3-2:17R \(\approx\)\(c\)-1:5H) [14]. It also exhibits weak superlattice reflections unique to the 2:17R phase, such as the [014]-2:17R at 2 theta of \(\sim\)31.5\({}^{\circ}\) and [024]-2:17R at 2 theta of \(\sim\)38.4\({}^{\circ}\). Meanwhile, the 1:3R phase is too small (the thickness is usually smaller than 10 nm) to be detected by conventional XRD [24; 25]. The [11\(\overline{2}\)] reflection at 2 theta of \(\sim\)32.2\({}^{\circ}\) belongs to the Sm\({}_{2}\)O\({}_{3}\) primary precipitates. During the standard preparation process of the 2:17-type Sm-Co based magnets, the Sm will be inevitably oxidated by the trace oxygen, resulting in the formation of the Sm\({}_{2}\)O\({}_{3}\) primary precipitates [26].
## 3 Results and Discussion
### Mechanical Properties of HONNs  
In this section, we focus on Young's modulus \(E\) and ultimate strength \(\sigma_{\rm u}\) of the HONNs. As shown in Figure 4a,b, they strongly depend on the relative density and loading direction and show approximately linear relationship in both Young's modulus \(E\) and ultimate strength \(\sigma_{\rm u}\) with respect to the relative density. The Young's moduli \(E\) (as shown in Figure 4a) increase with the increase of the relative density and the out-of-plane Young's moduli \(E\) are obviously higher than the in-plane Young's moduli, which is similar to traditional honeycomb materials [54]. The compressive moduli are slightly bigger than the tensile moduli in the in-plane direction, while they are almost equal in the out-of-plane direction. Similar to the \(E\), the ultimate strengths \(\sigma_{\rm u}\) (as shown in Figure 4b) increase with the increase of the relative density. Figure 4b also shows that the ultimate strengths of out-of-plane tension are significantly higher than those in other three cases. It is noted that, although the out-of-plane compressive Young's moduli \(E\) are higher than both the in-plane tensile and compressive Young's moduli, the ultimate strengths of out-of-plane compression have similar values with both the in-plane tension and compression owing to the smaller strain (around 0.05) corresponding to the ultimate strengths.  
Figure 3: Atomic snapshots at specified macro loading strain: (**a**, **d**, **g** and **j**) peak stress, (**b**, **e**, **h** and **k**) stage of collapse or strain-softening, (**c**, **f**, **i** and **I**) densification or rupture of samples. Identification of atomic configurations is based on common neighbor analysis (CNA) and four types of atoms are painted in different colors: green for face-centered-cubic (FCC) atoms, blue for body-centered-cubic (BCC) atoms, red for hexagonal close-packed (HCP) atoms and gray for non-12-coordinated atoms.  
As the relative density changes from 0.43 to 0.68, in the in-plane direction, the tensile modulus increases by 144.1% from 25.1 GPa to 61.28 GPa; the compressive modulus increases by 141% from 28.7 GPa to 61.17 GPa; \(\sigma_{\text{ut}}\) increases by 104.1% from 1.95 GPa to 3.69 GPa; and \(\sigma_{\text{uc}}\) increases by 155.3% from 1.79 GPa to 4.57 GPa. For the out-of-plane direction, the tensile modulus increases from 49.66 GPa to 90.06 GPa by 81.3%; the compressive modulus increases from 48.63 GPa to 91.06 GPa by 87.2%; \(\sigma_{\text{ut}}\) increases from 3.54 GPa to 6.24 GPa by 76.2%; and \(\sigma_{\text{uc}}\) increases from 1.96 GPa to 4.55 GPa by 132.1%. From Figure 4 it can be seen that the out-of-plane mechanical properties are generally superior to those of the in-plane direction. The main reason for this phenomenon is the in-plane direction has a weakness in ligament (marked in Figure 3b,c) which may induce stress concentration and lead to successive deformation and rupture of ligament. Compared with the in-plane direction, the samples have a more homogeneous deformation in the out-of-plane direction.
## 2 A New Approach for Optical Encoders
### Main Principle of the Encoder
An additional outer track with an offset by half a diode was provided. Hence, the interpolation of the expected sin/cos signals from the last two outer LSB tracks was possible, allowing increased relative resolution of the rotational angle between the two photodiodes. The two lenses (one for the segmented and another one for the Gray code detector) were designed by ray-tracing, using the optical design software OSLO(r) (Optics Software for Layout and Optimization). Because of the two different layouts of the photo detector, two different designs for the optical path were necessary. Figure 6 shows the optical path for both versions. For the first photo detector with the segmented photodiodes, a centric lens to focus the light onto the photo detector was needed. For the second photo detector with Gray code arrangement of the photodiodes, an additional lens was necessary to expand the diffracted spot in the radial direction and to illuminate all photodiode tracks. This led of course to a more complex optical path. The main advantage of the novel approach for optical encoders is that a lateral displacement or eccentricity of the disc does not influence the optical path. The encoder disc can be illuminated in its center, as well as on its outer diameter. Thus, hollow shaft encoders can be realized as well. A further advantage is that the functional principle of the encoder can be used to develop a modular design concept for optical encoders. When the design for the optical module is defined, the same optical module can also be used for different encoder diameters. Even the grating remains unchanged. Only the diameter of the disc needs to be adapted. A further aspect of this concept is the small dimensions with a comparatively high resolution. Thus, a low-cost encoder is possible with this novel approach. Figure 5: Two different layouts for the photodetector of the optical encoder. (**a**) Segmented arrangement of the photodiodes; (**b**) Gray code arrangement of the photodiodes. Figure 6: Design of the optical path for the encoder. (**a**) Optical path for the segmented photo detector; (**b**) optical path for the photo detector with Gray code arrangement of the photodiodes.
# Federated Learning for Secure In-Vehicle Communication
## 3 Federated Learning-Based Intrusion Detection Framework
### Adapted-BiLSTM Architecture (a-BiLSTM)  
BiLSTM is a variant of LSTM, which is a type of recurrent neural network that effectively learns long-term dependencies and addresses gradient issues, significantly enhancing classification accuracy [36]. Our a-BiLSTM architecture includes two BiLSTM layers followed by a dense layer, as shown in Figure 4. The first BiLSTM layer captures long-range temporal dependencies with 64 hidden units for each direction (forward and backward) and a return sequence parameter set to True, ensuring it outputs the entire sequence for  
Figure 3: Reference architecture of FL-IVN-IDS.  
each time step as input to the next layer. The second BiLSTM layer also has 64 hidden units per direction but uses a return sequence parameter set to False, outputting only the final state to summarize sequence information for subsequent layers. To prevent overfitting, we introduce regularization by applying a 50% dropout rate [37], randomly setting activations to zero during training. A fully connected layer with 128 units and ReLU activation follows, enabling the model to learn complex representations from the BiLSTM output. The output layer, consisting of the number of class units with a softmax activation function, predicts class probabilities. Although stacked BiLSTM architectures are commonly used for classification in the literature as in [38, 39], we designate this model as \"adapted\" due to its tailored design towards vehicular CAN bus data as well as deployment in real-time federated learning deployment. The model balances expressiveness and computational efficiency, making it suitable for edge deployment in OBUs (Onboard Units) with limited resources. Moreover, its structure was refined through empirical testing across three different vehicle datasets to ensure robust multiclass intrusion detection under various data distributions typical in federated learning. For this purpose, we have tempered explained the improvements in terms of context-specific optimization and real-world deployment issues.  
Table 1 provides the notations used in the paper for consistency and clarity.
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 2 Materials and Methods
### Scenarios
#### 2.2.2 Dynamic Scenarios
Thus, each subject was recorded walking for 2440 s, corresponding to 440 s for each of the sub-1 GHz frequencies and 340 s for the 2.4 GHz band, for each of the sender node positions. The wrist position was selected because it is a usual spot for motion sensors, such as smartbands, with step counting capabilities. This is not the case for the ankle position, so its characterization is useful in order to analyze its potential in this application.
_Appl. Sci._**2015**, \(5\), 1350-1378; doi:10.3390/app5041350  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**New Trends in Dental Biomechanics with Photonics Technologies**  
**[PERSON] 1,*, [PERSON] 1,2,\({}^{\dagger}\), [PERSON] 3,\({}^{\dagger}\) and [PERSON] 1,\({}^{\dagger}\)**  
* INESC-Porto and Faculty of Sciences of the University of Porto (FCUP), Rua do Campo Alegre, 687, Porto 4150-179, Portugal; E-Mails: [EMAIL_ADDRESS] (P.R.); [EMAIL_ADDRESS] (O.F.)
* CIDESD-ISMAI, Institute University of Maia, Avenida Carlos Oliviveira, Castelo da Maia, Porto 4475-690, Portugal
* Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Campus Universitario de Santiago, Aveiro 3810-193, Portugal; E-Mail: [EMAIL_ADDRESS]
* These authors contributed equally to this work.
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +351-220-402-301; Fax: +351-220-402-437.  
Academic Editor: [PERSON]  
_Received: 31 July 2015 / Accepted: 25 September 2015 / Published: 24 November 2015_  
**Abstract:** Engineering techniques used to evaluate strain-stress fields, materials' mechanical properties, and load transfer mechanisms, among others, are useful tools in the study of biomechanical applications. These engineering tools, as experimental and numerical ones, were imported to biomechanics, in particular in dental biomechanics, a few decades ago. Several experimental techniques have been used in dental biomechanics, like photoelasticity, ESPI (Electronic Speckle Pattern Interferometry), strain gages, and other kinds of transducers. However, these techniques have some limitations. For instance, photoelasticity and ESPI give the overall field pattern of the strain, showing the stress-strain concentration points. These methods cannot give an accurate measurement at all points. On the contrary, strain gages can be used to perform local measurements. However, as they use electrical resistances, their use is limited to perform _in vivo_ measurements. Optical fiber sensors have already been used in dentistry, for diagnostic and therapeutic purposes, and in dental biomechanics studies. Lasers have also been used in clinical dentistry for a few decades. Other optical technologies, like optical coherence tomography (OCT), became suitable for dental practice and nowadays it is perhaps one that has had more development in dentistry, along with lasers.  
**Keywords:** photonics technologies; photoelasticity; ESPI; OCT; lasers; fiber optic sensors; dental biomechanics
## 4 Proposed Orchestrator
### Description of the Architecture  
In addition to the conceptual model (Figure 2), we also propose the Orchestrator architecture. To understand the inherent complexity, this architecture of the 5G-Horizontal is illustrated in Figure 4. Its main components and modules are distributed in large blocks, which contain functionalities that express the proposals of the standardization entities, particularly the ETSI MANO framework architecture [40; 83].  
The network services are implemented from several layers of virtualization, constituting integrated blocks from the use of REST APIs. A description of the large blocks and the functioning of their internal structures are detailed below.  
Similar to others [83; 84; 85; 86; 83], this work follows the technical reference of the standardization bodies [35; 37; 40]. However, the architecture proposed herein presents an interesting combination between the features provided by Edge and Cloud Computing, thus enabling a promising differential for an efficient orchestration service.  
Several architectural proposals are found in the literature on vertical models, in which SLA guarantee mechanisms are proposed for specific applications through well-defined templates in an E2E architecture, such as 5 GTANGO, 5 GEx, 5G-Transformer, 5G EVE, 5G-VINNI, 5 GENESIS, 5 GROWTH, and 5G-VICTORI [66; 68; 69; 87; 88; 89; 90; 91]. Nevertheless, a strategy that has shown good results is to decouple and distribute computational resources between the edge and the cloud. For these cases, the integration between specific network functions (edge and cloud) helps to support different types of applications and services, mainly satisfying the requirements of QoS/QoE. The NECOS architecture, as well as 5 GIPAGODA, 5G NORMA, MATILDA, 5G-Crosshaul, and 5 GUK architectures, shows this integration in detail [83; 92; 93; 94].  
Our proposal brings together the integration characteristics reported in the aforementioned projects, with a greater focus on the selection of slices in the Edge cloud. This path has proven to be quite interesting in reducing latency and ensuring a better user experience, recovering the concept of **Always Best Connected**, in environments of heterogeneous and multi-provider technologies [95].  
In this article, we only highlight these modules since the functionality of all other entities is similar to those stipulated in the ETSI MANO framework. As illustrated in Figure 4, the architecture consists of the following functional blocks: _Multi-Provider Service Leader Plane, E2E Network Slice Orchestration Plane_, and _Logical Multi-Provider Network Slices_. The last block mentioned consists of network functions that comprise aspects of infrastructure management, the separation between control and data plane, and edge functions, especially the slice selection service.  
The mapping between components and modules from the Orchestrator architecture (Figure 4) to the conceptual model (Figure 2) is according to Table 2.