esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 2 Materials and Methods  
### Almond Samples and IR Spectra Collection  
In the present work, 40 almonds from Apulia, 80 from Calabria, and 40 from Sicily were analyzed, making a total of 160 samples. The analyzed almonds showed clear similarities in terms of form, size, and color, and these physical characteristics were not considered influential factors [16].  
The almonds did not present the husk (removed by the manufacturer). Prior to the analysis, the shells were rubbed with absorbent paper in order to remove any impurities possibly present on their surface. The samples were not washed (nor dried) before IR analysis. Consequently, no extensive pretreatment of the sample was necessary.  
Similarly, once the shells were broken, the almonds were cleaned with paper to remove any solid shell residues; then, they were analyzed without any further pretreatment.  
For each almond, at first, two replicated spectra were collected on the shell (as depicted in Figure 1A). Afterward, the shell was broken, and two replicate spectra were collected on the kernel (as depicted in Figure 1B). All signal replicates (regardless of how they were collected on the shell or on the kernel) were averaged before chemometric analysis. Eventually, for each almond, a shell spectrum and a kernel spectrum were available for the analysis. Therefore, a total of 320 spectra (160 for shells and 160 for kernels) were used to build chemometric models.  
The IR analysis was conducted using an attenuated total reflectance Fourier-transform infrared spectroscopy (ATR FT-IR) PerkinElmer Spectrum Two(tm) (PerkinElmer, Waltham, MA, USA). The spectrometer is equipped with a deuterated triglycine sulfate (DTGS) detector and a PerkinElmer Universal Attenuated Total Reflectance (uATR) accessory fitted with a single-bounce diamond crystal. Each acquisition is the result of ten averaged scans acquired in the spectral range of 4000-400 cm\({}^{-1}\) with an instrumental resolution of 4 cm\({}^{-1}\). The background was collected by exposing the diamond to air after cleaning it with absorbent paper soaked in methanol. After each scan performed on the shell, the  
Figure 1: Acquisition of spectra: (**A**) on almond shell; (**B**) on the kernel.  
ATR crystal was cleaned with absorbent paper soaked in methanol; subsequently, the spectrum of the kernel was acquired. Since the beam infiltrates the sample to a depth of a few microns and given that the thickness of the average shell is about 2 mm, the kernels do not contribute to the signals collected and the shells.
## 4 Detailed Design Method and CFD Model
The second-order central scheme coupled with the second- and fourth-order artificial dissipation terms were used to discretize spatial derivative terms. The pseudo-time derivatives were discretized by an explicit four-stage Runge-Kutta scheme. The local time stepping, multi-grid strategy, and the implicit residual smoothing techniques were applied to accelerate the process. Total pressure, total temperature, and flow angles were specified at inlet, and the static pressure was set as outlet condition. The non-slip adiabatic solid wall was applied and the mixing planes were used to treat rotor-stator interfaces. To capture the boundary layer in detail, \(y^{+}\) should be kept below 5. Figure 3 compares the CFD results with the test data [34]. The overall trend was accurately predicted by the numerical model. The relative error of the peak adiabatic efficiency was 0.26%. The CFD slightly underestimated total pressure ratio and mass flow rate by 0.01% and 0.10% at peak efficiency, respectively. The validation demonstrates that the CFD model can accurately predict the aerodynamic performance of compressor.
# Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection
## 3 Methodology
### Depthwise Separable Convolution  
[PERSON] et al. [10] and [PERSON] et al. [11] proposed Separable Convolutions layer (\"s-conv\" for short), which performed first a Depthwise Spatial Convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels.  
A standard 2D convolutional layer takes \(D_{H}\ \times\ D_{W}\ \times\ M\) as input feature map \(I\) and produces a \(D_{h}\ \times\ D_{w}\ \times\ N\) output feature map \(O\) where \(D_{W}\) and \(D_{H}\) are the spatial width and height of a input feature map, \(M\) is the number of input depth, \(D_{w}\) and \(D_{h}\) are the spatial width and height of a output feature map and \(N\) is the number of output depth.  
Kernel \(K\) performs convolution on input feature map (with zero padding and stride one), has size \(D_{K}\ \times\ D_{K}\ \times\ M\ \times\ N\), where \(D_{K}\) is the spatial dimension of the kernel (assumed to be square) and \(M\) is the number of input channels and \(N\) is the number of output channels as defined. Normal convolutions have the computational cost of:  
\[L_{conv2d}=D_{K}\times D_{K}\times M\times N\times D_{h}\times D_{w} \tag{3}\]  
The computational cost depends multiplicatively on the number of input channels \(M\), the number of output channels \(N\), the kernel size \(D_{K}\ \times\ D_{K}\) and the feature map size \(D_{h}\ \times\ D_{w}\).  
Depthwise Separable Convolution splits a kernel into 2 separate kernels that do two convolutions: the Depthwise Spatial Convolution and the pointwise convolution. Depthwise Spatial Convolution is the channel-wise \(D_{K}\ \times\ D_{K}\) spatial convolution. Pointwise convolution is the \(1\times 1\) convolution to change the dimension. Separable convolutions have the computational cost:  
\[L_{separable\ conv2d}=D_{K}\times D_{K}\times M\times D_{h}\times D_{w}+M \times N\times D_{h}\times D_{w} \tag{4}\]  
Performing division, the computational reduction is:  
\[\frac{\text{L}_{separable\ conv2d}}{\text{L}_{conv2d}}=\frac{1}{N}+\frac{1}{D_ {K}{}^{2}} \tag{5}\]
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 9 IoT and Smart Contracts on Distributed Ledgers  
### Motivations of Blockchain Utilization in Industrial IoT  
Industrial IoT has extra concerns on data security and privacy, as discussed earlier. This is especially true within multi-stakeholder environments where parties do not necessarily trust each other, but are eager to have business relationships. Production-focused sectors of the industry tend to build on established, safe, and secure methods of info-communication technologies; as both introduction and the in-production error correction has high stakes and price.  
The technical requirements of real-time operation, security and safety, high reliability, complexity, engineering efficiency are already available for the early adopters of IIoT solutions. On the other hand, non-technical aspects, such as return of investment and trust are hard to justify when introducing new technologies with such a great paradigm shift as digitization for Industry4.0. Although trust among new partners is built through the involvement of legal and financial authorities, this do not cover end-to-end supply chains--just overlapping ecosystems. This is a relatively slow, but seemingly necessary process, which, again, will not cover trust among all parties in the supply chain, just those who interact. This also means that stakeholders at the end of the logistic chain have no easy way to go after initial vendors, their procedures, not to mention their failures.  
The CIA triad can be further enforced by using blockchain technologies: confidentiality is provided by the Public Key Infrastructure (and methods alike), Integrity is a core concept ensured by the immutable feature, whereas Availability is there due to massive distribution of the ledger. Furthermore, traceability and reliability of IoT data are also inherited by the immutable distributed ledgers--so time-stamped data inclusion and modification can be traced back, and data quality of the data remains trustworthy [96].  
The immutable feature of distributed ledgers, and their verification through various blockchain technology methods make it possible to leave out third parties--central authorities--who are involved merely to build trust. Together with smart service contracts, the use of private blockchains within industrial partnerships or ecosystems make transactions fast, secure, safe, trusted, validated and immutable throughout the various supply chains.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 2 Materials and Methods
### Definition and Characteristics of Raw Bluetooth Signals
These features make the raw signal a rich source of information that can be used as a radio frequency fingerprint because they reflect unique device-specific attributes. Figure 2: Signal segmentation in the Rusins dataset: (**a**) Full recording of a wearable device. (**b**) 5\(\upmu\)seg segment extracted from the full recording. However, the raw signal also has a wide range of frequencies, often containing unwanted frequencies and noise that can obscure the critical features needed for accurate classification. Without signal preprocessing, such as filtering or scaling, the quality of the raw signal can be degraded, resulting in reduced performance of machine learning models. Therefore, signal preprocessing techniques such as bandpass filtering are essential to isolate the primary signal components and ensure that the raw signal is suitable for further analysis.
###### Abstract  
Air pollution in urban areas has increased significantly over the past few years due to industrialization and population increase. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based examination for forecasting Air Quality Index (AQI) values, employing two different models: a variable-depth neural network (NN) called slideNN, and a Gated Recurrent Unit (GRU) model. Both models used past particulate matter measurements alongside local meteorological data as inputs. The slideNN variable-depth architecture consists of a set of independent neural network models, referred to as strands. Similarly, the GRU model comprises a set of independent GRU models with varying numbers of cells. Finally, both models were combined to provide a hybrid cloud-based model. This research examined the practical application of multi-strand neural networks and multi-cell recurrent neural networks in air quality forecasting, offering a hands-on case study and model evaluation for the city of Ioannina, Greece. Experimental results show that the GRU model consistently outperforms the slideNN model in terms of forecasting losses. In contrast, the hybrid GRU-NN model outperforms both GRU and slideNN, capturing additional localized information that can be exploited by combining particle concentration and microclimate monitoring services.  
air quality index; air pollution; neural networks; deep learning; forecasting; recurrent neural network; edge computing; cloud computing Article
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 2 Inception of the Digital Twin Concept
It is a digital representation of the active product service system, which comprises preset characteristics, conditions, behaviors and properties [14]. Along with this concept, the Internet of Vehicles (IoVs) has also been developed with the integration of Vehicular Ad hoc Networks (VANETs) and the Internet of Things (IoTs) [15; 16]. These were more focused on land-based locomotives, but the notion is widely accepted by many industrial sectors. IoT has supported the real-time data handshake component of DT, which has been the basement of existing topology. These also incorporate the diagnostics and prognostics embedded within the system architecture along with optimization of the process [17]. Risk analysis and accident prevention with DT support is another frontier that has already been adopted by the aviation industry. The system verification of the Boeing 737 Max after initial failures is a great example [18]. It ultimately promotes the concept of Smart Products (SP) in various phases of the product life cycle, which benefits the stakeholders in the user groups of the product or asset [19]. In the industry, DT is used under different names such as cyber objects, digital avatars, etc. The data flow between the DT and the physical unit will depend upon the purpose of the DT in the industry, which uses cyber objects or digital avatars. Accordingly, the nomenclature of DT has been proposed based on the two-directional data link between the DT and the actual unit. [PERSON] et al. have categorized the digital counterparts of a physical object based on the data flow into three types: Digital Model, Digital Shadow, and DT [20] (Figure 2). * Digital Model: There is no automated data exchange between the two units. Data integration is done manually to synchronize. * Digital Shadow: There is only one-way data flow from the physical object towards the digital counterpart. The data arriving from the physical asset will update the digital object, but not vice versa. This requires offline actions with Human-in-the-loop (HITL) interaction between the physical object and the digital model. * Digital Twin: Fully integrated data flow is available where a two-way automated data link is established. Both units are in a real-time synchronized state and the physical unit can be influenced by the digital object automatically.
## 1 Introduction
[22] studied two different patterns, i.e., line and grid under tensile and impact loads. The former pattern showed higher tensile and impact strength. The above analysis indicates that only limited studies on the raster angles and their combinations, such as \(0/90^{\circ}\) and \(45/-45^{\circ}\), have been conducted by researchers under flexural loads. Whereas the work related to the effect of infill patterns has only been performed under tensile loads; the other loading conditions have not yet been explored. Therefore, there is a need to study the behavior of different raster angles and infill patterns under flexural loading conditions. The reason behind the evaluation of the in-plane and edgewise (transverse) bending moduli was that there are many applications where the beams are subjected to both types of bending loads. As discussed above, the most common of these applications are in aerospace, especially in aircraft wing spars which are the main loading carrying component of an aircraft wing [23]. They are subjected to in-plane and edgewise flexural loading during the flight of an aircraft, as illustrated in Figure 2. The flexural loads are higher than the other loads on a spar during flight [24]; therefore, it was necessary to determine the raster angle and infill pattern which gave the highest flexural properties. Hence, a range of samples constituting singular as well as a combination of raster angles, i.e., \(0^{\circ}\), \(90^{\circ}\), \(60^{\circ}\), \(0/90^{\circ}\), \(45/-45^{\circ}\), \(60/30^{\circ}\), and \(75/15^{\circ}\), were studied experimentally along with five different infill patterns. The raster angles representation with the help of flatwise samples is presented in Figure 3. The results have been quantified in terms of maximum flexural load, flexural modulus, flexural strength, and fracture strain under flexural load. Figure 2: Wing spar under in-plane and edgewise flexural loading.
## 4 Discussion
### Recovery Cycle  
The weekly load in preseason was significantly higher than the first round. This is in line with what was obtained by [PERSON] et al. [9], who found the highest weekly loads at the beginning of the season. In the preseason, to prepare the players for the competition, the coaches tend to include more work sessions and of more volume to condition the players properly.  
The more experience a player has, the more game load she accumulates. This is basically due to the age of the players, since the most experienced are also the most veteran and therefore the ones who suffer the most to maintain their physical level. This is in agreement with [PERSON] et al. [9], who found that experience is in line with the age of the players. As seen in soccer, older players have poorer physical performance during games due to their lower physical abilities [61; 62]. The preparation of a squad of players must take into account age (linked to experience). A coach must know the technical-tactical performance that an experienced player contributes; this player often does not demonstrate the best physical performance.  
The analyses carried out on the pre-game recovery show how the weekly load has a significant relationship with recovery. The players who report the most load during the week are the ones who give the worst pre-game recovery values, because they are more fatigued. These data are similar to those obtained by [PERSON] et al. [9] and [PERSON] et al. [35].  
The players with more experience are those who reported better recovery values. This may be due to the fact that they know their body better and are able to dose themselves better during the week to arrive more rested on game day. However, the results of [PERSON] et al. [9] and [PERSON] et al. [35] are contrary to this: the most experienced players are those who gave the worst recovery values, due to their lower physical capacities.  
The season phase and the recovery cycle also have a significant influence on pre-game recovery. As the season progresses, the players report increasingly worse pre-game recovery values, due to the fatigue that accumulates over the weeks. When the recovery cycle is short, recovery values are worse. However, [PERSON] et al. [9] found no significant relationship between these variables, and [PERSON] and [PERSON] et al. [63] also found no differences in the recovery values in the final phase of the season. This may be because the teams analyzed in those studies had a more relaxed schedule in the final phase of the season. These data highlight the importance of load control, and mechanisms must be used to determine the acute:chronic relationship.  
The recovery values were higher in relation to the lower the level of the last opponent they had faced. When playing low-level teams, the demands were lower, and that resulted in players reaching the next game in better physical condition. This is in agreement with what was obtained by [PERSON] et al. [9] and [PERSON] et al. [35]. Games against high-level rivals, being more demanding, end up having an impact on the next game as well, as the players arrive more fatigued.
## 7 Unbalanced PSI-CA Protocol Based on Dual-Cloud Assistance
### Protocol Process  
#### 7.2.1 Preprocessing  
1. Discuss security parameters: Each party discusses the necessary security parameters--the large prime \(q\) used in DH encryption and the small health app developer's public key \(pk_{c}\) required for the Paillier encryption system. The small health app developer and the large medical institution negotiate the creation of hash function \(H\).
2. The small health app developer negotiates with the large medical institution to create an obfuscated dataset \(Y^{\prime}\). This dataset is completely useless data, which means that its elements cannot belong to either the small health app developer or the large medical institution collection.
3. Small health app developer sends \(E_{pk_{c}}(a)\): The small health app developer generates its private secret number \(a\) and sends \(E_{pk_{c}}(a)\) to the large medical institution.
4. Large medical institution generates private key: The large medical institution creates its private key \(\alpha\), used for the DH encryption algorithm.
5. Data scrambling: The small health app developer and the large medical institution each shuffle their respective datasets.
6. Small health app developer calculates hashes and generates random numbers: The small health app developer computes \(h_{j}=H(y_{j})\) and generates \(n_{2}\) random numbers, \(\beta_{j}\), \(\omega_{j}\), \(r_{1j}\), \(r_{2j}\), and computes \(r_{1j}h_{j}\), \(r_{2j}h_{j}\), where \(r_{1j}+r_{2j}=a\).
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 1 Introduction
When using these systems, the operator can monitor the main parameters of any light from a control room or a mobile unit. The obtained data can be processed, allowing for statistical calculation of the consumption, state of the lamp, voltage, failures, _etc._ In this way, the average repair time can be reduced. Another interesting parameter could be the arc voltage level, which entails the change from corrective maintenance to predictive maintenance, meaning economic savings in maintenance costs. To optimize the control, the over-the-air management system needs to support bidirectional communication between the user and the ballast. This management system is implemented using a communication system and a control protocol for the lighting. The communication system can be wired or wireless. The wired communication systems that are normally used include Ethernet-based systems and those based on optical fiber or power-line carrier (PLC). As wireless standards, we can find, among others, GSM/GPRS, RF, Wi-Fi(tm), IEEE 802.15.4(tm) and ZigBee(tm). The latter two are responsible for the increase of wireless sensor networks (WSNs) [8; 9]. Standard IEEE 802.15.4 is aimed at low-rate wireless personal area networks (WPANs) [10]. Its objective is to standardize the two lower layers of the communication protocols between open protocols, _i.e._, the physical layer and the MAC layer. On the other hand, ZigBee defines the upper layers, the network and the application layers. For the lighting-control protocol, it is possible to choose between open protocols, such as TCP/IP, BACNet(tm), DMX 512(tm), LONWorks(tm), X-10(tm), 0-10 V and DALI(tm), and proprietary protocols. From the previously mentioned protocols, the system described in this work uses protocol IEEE 802.15.4 for wireless communications and the DALI protocol to control the lamps. The Digital Addressable Lighting Interface (DALI) is designed to digitally control electronic ballasts and luminaries equipped with this technology [11]. The interface and protocol are defined by standard IEC 60929/EN 60929 Section E.4, and were modified by IEC 62386, which also includes other applications for DALIbesides lighting and expands the types of lamps to _high-intensity discharge_ (HID) lamps, halogen lamps, incandescent bulbs, LEDs, _etc_. The paper is organized as follows: Section 2 provides a short review of the state of the art in lighting control systems; Section 3 presents an overview on the architecture of the system; Section 4 describes the hardware and software implementation of the wireless node; Section 5 shows the results of some of the implemented tests and installations; and finally, Section 6 provides conclusions.