esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 2 Materials and Methods  
A block diagram of BIS instrumentation system which is proposed in this research is given in Figure 1. It is built in several integrated modules, and can be grouping into three main units, i.e., AC current source, data acquisition, and software. The unit of AC current source is a stable current source with selectable frequency within a certain frequency range.  
In the implementation, an AC current source with a certain frequency is injected into the sample through a reference resistor (R-Ref). Function of R-Ref is to guarantee that the current which is recorded by the data acquisition system is the actual current received by the sample. In this case, the actual  
Figure 1: Block diagram of the developed BIS instrumentation system.  
current value is the voltage value read by IA1 divided by R-Ref, while the sample voltage arising due to injection current is read by IA2. Next, the outputs of IA1 and IA2 are forwarded to the gain phase detector module (GPD-AD8302). GPD performs an internal process to produce the gain in dB of (_V/I_) and the phase difference, the both in form of the equivalent voltages. These two signals are then converted into digital data by the ADC contained in the ATmega microcontroller, before being sent to the computer (PC). On the PC, the data is calculated to produce an impedance value, and plotted directly as single point on the Bode and Nyquist charts. This process is repeated for other frequencies until one full frequency cycle is completed.  
Here, the software is developed and installed on the microcontrollers and PC, used for three purposes. The first is to control the working function of the hardware as a whole, specifically the selection of the current value and the desired working frequency range. The second is to calculate the complex impedance of the sample based on data from GPD, and the third is to present the complex impedance dispersion of the sample in the form of Bode and Nyquist plots in real time.
# Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence
## 2 Materials and Methods
### Field Tests
#### 2.3.2 Evaluation of the Spraying Quality and the Amount of Spray Liquid (Water) Used
* \(\overline{d}\) is the average distance (offset) for the single test, defined as: \(\overline{d}=\frac{\sum_{i=1}^{N}}{N}\), where \(d_{i}\) is the offset for every single water-sensitive sample belonging to the considered test and region. * \(\sigma_{d}=\sqrt{\frac{\sum_{i=1}^{N}\left(d_{i}-\overline{d}\right)^{2}}{N}}\) is the standard deviation of distance (offset) for the given test and region. Another important aspect of the analysis is the amount of mixture (water) distributed. It can be estimated, knowing the flow rate of each nozzle, the speed at which the rover moves and, to refer to the area covered, the distance between the nozzles. Such quantity, called \"coverage\", is defined as  
\[C=\left(\frac{l}{ha}\right)=\frac{Q}{v\Delta s}\times 10^{4} \tag{1}\]  
where:  
\(Q\left(\frac{l}{s}\right)\) is the nozzle flow rate  
\(v\left(\frac{m}{s}\right)\) is the travelling speed of the rover  
Figure 3: Some examples of single water-sensitive papers extracted by extraction algorithm (on the left in each pair) and black and white images obtained after transforming all ‘blue pixels’ to ‘black’ (on the right in each pair). \(\Delta s(m)\) is the distance between the nozzles. To calculate the flow rate, the following equation is adopted:  
\[Q=const.\times\sqrt{\Delta p}, \tag{2}\]  
where:  
\(\Delta p\) is the differential pressure between the pressure of fluid inside the nozzle and ambient pressure; this differential pressure is directly obtained from the sensor. Using table data for the flow at \(\Delta p=300\ kPa\), it is possible to express:  
\[Q(\Delta p)=Q(300\ kPa)\times\sqrt{\frac{\Delta p}{300\ kPa}}. \tag{3}\]  
For choosing the optimal set of spraying parameters, both the following conditions should be met:  
* Average coverage \(\overline{c}\ (\%)\) should be acceptable (not too low, and with no or very few insufficient papers, Figure 4). * Coverage in liters per hectare \(C\ \left(\frac{L}{\hbar t}\right)\) should be as low as possible. Figure 4: Degree of coverage of water sensitive papers [19]. It is intuitively clear that these parameters are, roughly speaking, inversely correlated.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Maintenance Reachability Analysis-Oriented 3D Reconstruction of Maintenance Scene  
#### 3.2.1 Product Model Reconstruction  
For the purpose of simplifying the judgment of the spatial relationship in the subsequent steps, the product models need to be reconstructed first. In the proposed methodology, the arm that was originally regarded as a cylinder is treated as a line segment, then the thickness of arm, which is the radius of cylinder, is superposed on the product models, as the surface of each product model expands outward by the same thickness, and the product models are reconstructed. In this way, the complex body-to-body intersection detection is translated into simpler line-to-body intersection detection. We used a spherical object as an example.  
Figure 4 is a schematic diagram of judging the spatial relationship between a forearm and a spherical object. In this figure, the cylinder with radius \(r_{1}\) represents the forearm and the sphere object with radius \(R_{2}\) represents the product that needs to judge the spatial relationship with the forearm. The thickness of forearm \(r_{1}\) is added to the sphere, as the surface of the sphere expands outward, \(r_{1}\) and the sphere's radius becomes \(r_{1}+R_{2}\). The forearm is simplified from a cylinder to a line segment. The spatial relationship between the forearm and the sphere can be obtained by detecting the intersection between the expanded sphere and the line segment. Therefore, the detection efficiency will be improved by this reconstruction.  
During the judgements of the relationship between the arm and products, the forearm and the upper arm need to be considered simultaneously. Owing to the different thicknesses of these two segments, two corresponding calculation scenes should be built as follows. Assuming the thicknesses of the forearm and the upper arm are \(r_{1}\) and \(r_{2}\), respectively, each product's surface expands outwards, for \(r_{1}\) and \(r_{2}\) to conduct corresponding intersection detection with the forearm and the upper arm separately.
## 4 Performance Evaluation
### Simulation Results and Analysis  
The proposed routing and other traffic-aware routing protocols, GSR and GyTAR, are evaluated based on the following metrics:  
* Packet delivery ratio: the percentage of packets successfully delivered from the source to the destination.
* End-to-end delay: the time it takes for packets to traverse the network from the source to the destination.
* Bytes overhead is the percentage of total control packet bytes incurred before the simulation fully runs, and the packet arrives at its destination; it is calculated by accumulating the number of bytes in control packets.
## 3 Security Communication Model of OPC UA Based on Whirlpool  
### Architecture of Model  
The target of this model is to build a security communication channel between client and server in OPC UA. This channel is always active during the process of communication, and it guarantees the integrity and authentication of all the messages exchanged. This means that the client and server need to authenticate each other only once. In this model, the client sends and receives requests and responses of corresponding services using its APIs. The server mainly provides two types of services for the client, one is to accept the connection requests and notification subscription requests from the client, the other is to publish the occurrence of some events to the client, such as alarms, changes of data values, events and execution results of programs. The CA is mainly responsible for building, issuing and managing certificates for clients and servers. There may be several clients and servers included in this model. Each client may be connected to one or more servers, and each server may be connected to one or more clients. An application may include two parts of modules performing functions of client and server respectively, hereby it can meet the requirement of connecting to other clients and servers. The architecture is illustrated in Figure 3.  
Figure 2: Comparison between Whirlpool and SHA-1.
## 4 Characteristics and Challenges of a Heterogeneous Private Edge Cloud System
### Heterogeneous and Multilayer Communication and Network Infrastructure  
A heterogeneous private edge cloud consists of stationary and mobile devices. Devices equipped with multiple wireless communication technologies are common, and they will become more persistent with the deployment of 5G networks. Wireless communication technologies have diverse features, and they also differ in terms of bandwidth, latency, energy consumption, communication range, reliability, and network topology. Devices may communicate via infrastructure-based wireless LAN technologies, infrastructure-less wireless LAN technologies, or both. These characteristics enable the creation of a communication and network infrastructure with multiple and overlapping communication topologies, diverse source-to-destination links, and dynamic topologies and links due to the existence of mobile nodes.  
Given the aforementioned characteristics, heterogeneous and multilayer communication and network infrastructure also introduce several challenges, as follows:  
* Development of communication and energy consumption cost estimation models and link quality and link lifetime estimation models for heterogeneous wireless communication technologies;
* Design of efficient discovery and monitoring protocol or set of protocols for a multi-layer network infrastructure that is characterized by multiple and overlapping communication topologies, diverse source-to-destination links, and dynamic topologies and links;
* Development of routing and network management protocols capable of selecting routes and supporting data transmission services based on a diverse quality of service requirements over a multilayer network infrastructure; and
* Design of a network layer that provides a unified and easy-to-use interface to higher layers.
## 7 Defenses against Privacy Attacks
### Homomorphic Encryption  
Homomorphic encryption is a cryptographic technique that enables computations on encrypted data without decryption, crucial for preserving data privacy and security in FL [139]. In FL systems, homomorphic encryption enables the aggregation of model updates from multiple participants while keeping individual contributions encrypted [140]. This method ensures that sensitive data remain protected throughout the aggregation process as computations are performed on ciphertexts rather than plaintexts. By supporting operations like addition and multiplication on encrypted data, FL servers can derive a global model without accessing raw, sensitive information from participating clients [141]. This capability is essential for maintaining confidentiality and fostering trust in FL environments, empowering data owners to collaborate on model training tasks while retaining control over their information [142]. Figure 15 displays the block representation of homomorphic encryption.  
For instance, the authors in [143] introduced Vertical Federated Learning (VFL), where data are partitioned among parties, each holding different features of the same samples. To safeguard privacy, communications are protected using techniques such as homomorphic encryption, ensuring that individual gradients or intermediate results remain inaccessible. The framework addresses privacy threats from attackers attempting to infer private sample labels from batch-averaged gradients, employing methods like differential privacy and gradient sparsification. Additionally, a confusional autoencoder (CoAE) is utilized to obscure true labels with \"soft fake labels\", preserving accuracy while concealing sensitive information.  
Similarly, the Privacy-Enhancing Federated Learning (PEFL) framework proposed in [144] emphasizes data privacy and robustness against attacks in federated settings. PEFL employs homomorphic encryption (HE) to maintain the confidentiality of training data and local gradients throughout the FL process. Users encrypt their gradient vectors using the cloud platform's public key before transmission, ensuring that secure aggregation protocols can operate within an encrypted domain without revealing individual user data. PEFL also defends against adversarial poisoning attacks through adaptive federated aggregation techniques, leveraging coordinate-wise medians to benchmark gradient similarity and mitigate the influence of outliers on model training. This approach enhances PEFL's resilience against data poisoning attempts, reinforcing the integrity and reliability of collaborative learning in federated environments.
## 3 Discussion
A total of six characteristic VOCs were detected from the fermentation broth of the strain, which were 3-methyl-1-butanol, 3-methyl-2-pentanone, 5-methyl-2-hexanone 2-heptanone, 2,5-dimethylpyrazine and 6-methyl-2-heptanone. The nematicidal mechanism and chemical structure of bacterial VOCs are similar to those of traditional fumigants, and the VOCs can act on nematodes through direct contact, fumigation, repellent or attraction [29; 30; 31]. For example, the VOCs produced by _Virgibacillus dokdonensis_ MCCC 1A00493 showed several activities against _M. incognita_. Acetaldehyde displays the activities of attraction, direct contact, and fumigation against root-knot nematodes, whereas ethylbenzene and 2-butanone show attraction and repellent activities, respectively [32]. _Paenibacillus polymyna_ KM2501-1 caused the 87.6% mortality of _M. incognita_ in vitro and produced eleven VOCs. Of these, furfural acetone and 2-decanol were able to attract and then kill _M. incognita_ through fumigation and direct contact [33]. We evaluated the way in which the VOCs of _B. velezensis_ GJ-7 act on _M. hapla_, including the direct-contact nematicidal activity against the _M. hapla_ J2s, the direct-contact inhibition activity against egg hatching, the fumigation activity against the _M. hapla_ J2s and eggs hatching, and the chemotaxis activity against the _M. hapla_ J2s. The results showed that all the six VOCs had a certain level of direct-contact inhibition and fumigation inhibition activities against J2s and the eggs of _M. hapla_. In addition, the results of chemotaxis experiment showed that all of the six volatile compounds displayed repellent activity against root-knot nematodes. Based on the above data, we predicted that the VOCs from _B. velezensis_ GJ-7 have three modes to control root-knot nematode (Figure 6): 1 fumigate or act directly on eggs to inhibit egg hatching; 2 kill the J2s by fumigation or direct contact; 3 display repellent activity toward J2s. This research further demonstrated the mechanism of nematicidal activity of _B. velezensis_ GJ-7 VOCs against _M. hapla_. Figure 6: Action modes of VOCs of Bacillus velezensis GJ-7 against root-knot nematodes.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis  
The simulated star images are used to test the performance of various identification algorithms, selecting some representative star identification algorithms as comparisons for our algorithm. The improved triangle algorithm [8], radial axial algorithm [13], grid algorithm [10], pyramid algorithm [7], and dynamic angle matching [19] are compared with the algorithm presented in this section.  
Additionally, to make the simulated star images more closely resemble actual star images, factors such as positional noise, false stars, and magnitude noise are introduced during the simulation process. The identification rates of the algorithms under different conditions are further analyzed to assess their robustness. Finally, real star images transmitted by a specific model of satellite star sensor are used for testing to validate the usability of the algorithm.  
All experiments and algorithms were simulated in PyCharm CE (V2021.3) with Python 3.11 on a PC with a 2.30 GHz Intel Xeon Gold 6139 CPU with 32 GB RAM and an NVIDIA Quadro P4000 GPU with 8 GB RAM.
## 6 Conclusions  
In this paper we proposed Energy Efficient D2D over Single Frequency Network (EED2D-SF), a resource allocation algorithm for multicast service delivery that couples the conventional multicast scheme with D2D communications. It exploits the Single Frequency concept for the D2D transmission, with the aim to increase the network energy efficiency in 5G New Radio Networks. A simulation campaign has been conducted in case of file downloading (i.e., software upgrade/update) in a mMTC scenario by taking into account all frame configurations foreseen by the 3 GPP standard.  
Simulative results demonstrate that, in a context where the energy consumption of mobile devices is an increasingly important concern, the proposed EED2D-SF is the most performing algorithm in terms of network energy efficiency with respect to other compared solutions.  
Figure 6: Network Energy Efficiency under increasing number of multicast MTC devices.  
Figure 7: ADR under increasing number of multicast MTC devices.  
An extremely interesting future direction of investigation is the analysis of a mobile scenario. In fact, as a future work we intend to analyze the feasibility of implementing the proposed solution in a V2X scenario.  
[PERSON], F.R., and [PERSON] conceived the idea; [PERSON] and F.R. designed and implemented the proposed solutions, performed the experiments; all the authors analyzed the data and wrote the paper; furthermore, all the authors reviewed the writing of the paper, its structure and its intellectual content.  
This research received no external funding.  
The authors declare no conflict of interest.
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 3 The Proposed Method  
According to the above description, the mission profile of the mechanical system will seriously affect its service life. Therefore, in the process of life prediction, the accuracy of life prediction can be improved by considering a specific mission profile in the future. In order to achieve the life prediction with considering the future mission profile, the main implementation steps of the proposed method are as follows.  
Step 1: Select several similarity reference samples from the historical samples through measuring the similarity between the samples to be predicted and all the samples in the historical sample.  
Step 2: Operating condition partitioning. Given a similar reference sample sequence \(\mathbf{ST}^{i}=\{x_{1},\ldots,x_{m}\}\), partition this sequence into \(C\) new sequences \(\left\{\mathbf{OT}^{i}_{j}\right|j=1,\ldots,C\}\) according to the operating condition, where \(\mathbf{OT}^{i}_{j}\cap_{j\
eq j^{\prime}}\mathbf{OT}^{i}_{j}=\varnothing\). Where \(m\) is the number of similar reference samples and \(C\) is the number of operating conditions.  
Step 3: Construct the reference degradation model by using RVM.  
Step 4: Calculate the proportions of each single operating condition in the future mission profile, then predict the life through weighted aggregation.  
The basic framework of the proposed method is shown in Figure 2. The proposed method has two critical steps: The construction of the reference degradation model and the life prediction according to the future mission profile. The two steps are introduced in detail as follows.  
Figure 2: The process of the proposed method. The proposed method consists of two main processes: The construction of the reference degradation model and the life prediction. RVM, relevance vector machine.
## 2 Material and Methods
### Hemolytic Activity of Ch. sorokiniana Methanol Extracts  
Hemolytic activity was investigated to determine the safety of cytotoxic extract components and concentrations towards human erythrocytes to be used in in vivo studies.  
For this, we collected blood from a healthy volunteer in EDTA tubes (BD Vacutainer K2 EDTA; Becton Dickinson, Franklin Lakes, NJ, USA) and erythrocytes were washed three times with PBS (pH 7.2). We then prepared a 5% red blood cell suspension in sterile PBS and evaluated the hemolytic activity of _Ch. sorokiniana_ methanol extracts and CFs.  
For hemolytic activity tests, 100 uL of crude methanol extract and CFs were incubated at the concentrations mentioned above with 100 uL of a 5% human erythrocytes suspensionin 96-well plates, and incubated for 30 min at 37 \({}^{\circ}\)C. Then, cells were recovered and centrifuged, and the OD at 540 nm of the hemoglobin released was recorded, using PBS as a negative control, and distilled water as a positive control [17]. The average value was calculated from triplicate determinations. The percentage of hemolysis for each sample was calculated using the following equation:  
\[\%\text{Hemolysis}=\left(\frac{\text{ODTreatment}}{\text{ODPositivecontrol}} \right)\times 100\]
# Object Detection for Brain Cancer Detection and Localization
## 2 The Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **From** & **n** & **Params** & **Module** & **Arguments** \\ \hline
0 & \(-1\) & 1 & 928 & modules.Conv & [3; 32; 3; 2] \\ \hline
1 & \(-1\) & 1 & 18,560 & modules.Conv & [32; 64; 3; 2] \\ \hline
2 & \(-1\) & 1 & 29,056 & modules.C2f & [64; 64; 1, True] \\ \hline
3 & \(-1\) & 1 & 73,984 & modules.Conv & [64; 128; 3; 2] \\ \hline
4 & \(-1\) & 2 & 197,632 & modules.C2f & [128; 128; 2, True] \\ \hline
5 & \(-1\) & 1 & 295,424 & modules.Conv & [128; 256; 3; 2] \\ \hline
6 & \(-1\) & 2 & 788,480 & modules.C2f & [256; 256; 2, True] \\ \hline
7 & \(-1\) & 1 & 1,180,672 & modules.Conv & [256; 512; 3; 2] \\ \hline
8 & \(-1\) & 1 & 1,838,080 & modules.C2f & [512; 512; 1, True] \\ \hline
9 & \(-1\) & 1 & 656,896 & modules.SPPF & [512; 512; 5] \\ \hline
10 & \(-1\) & 1 & 0 & upsampling.Upsample & [None, 2, ‘nearest’] \\ \hline
11 & \([-1,6]\) & 1 & 0 & modules.Concat & [1] \\ \hline
12 & \(-1\) & 1 & 591,360 & modules.C2f & [768; 256; 1] \\ \hline
13 & \(-1\) & 1 & 0 & upsampling.Upsample & [None, 2, ‘nearest’] \\ \hline
14 & \([-1,4]\) & 1 & 0 & modules.Concat & [1] \\ \hline
15 & \(-1\) & 1 & 148,224 & modules.C2f & [384; 128; 1] \\ \hline
16 & \(-1\) & 1 & 147,712 & modules.Conv & [128; 128; 3; 2] \\ \hline
17 & \([-1,12]\) & 1 & 0 & modules.Concat & [1] \\ \hline
18 & \(-1\) & 1 & 493,056 & modules.C2f & [384; 256; 1] \\ \hline
19 & \(-1\) & 1 & 590,336 & modules.Con & [256; 256; 3; 2] \\ \hline
20 & \([-1,9]\) & 1 & 0 & modules.Concat & [1] \\ \hline
21 & \(-1\) & 1 & 1,969,152 & modules.C2f & [768; 512; 1] \\ \hline
22 & \([15,18;21]\) & 1 & 2,116,822 & modules.Detect & [2; 128; 256; 512]] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architecture of the YOLOv8s model for object detection. Situated between the backbone and the head is the neck, which consists of a series of layers that blend and merge image features before forwarding them to the prediction stage. This intermediate step allows for effective combination and transformation of features, enhancing the network's ability to make accurate predictions. Overall, the YOLO architecture integrates these components to enable comprehensive feature extraction, feature fusion, and prediction steps, contributing to its object detection capabilities. Once the YOLO model is trained, it will be able to perform the following operations on unseen brain MRIs:  
1. Classify the image as cancerous;
2. Detect the area (i.e., localize) related to brain cancer;
3. Assign a probability of the cancer presence.