esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
### Acronyms  
ACAlternating Current  
BESSBattery Energy Storage System  
DCDirect Current or Continuous Current  
DODDepth of Discharge  
EMSEnergy Management System  
ESSEnergy Storage System  
EVElectric Vehicle  
EVCSElectric Vehicle Charging Station  
FLFuzzy Logic  
GAGenetic Algorithm  
GHGGreenhouse Gas  
MFMembership Function  
RBRule-Based  
ROIReturn of Investment  
SOCState of Charge
## 3 Case Study: Dairy Industry
### Transportation Capacity  
The transport of products between factories and even the distribution center \(f_{5}\) is carried out through a heterogeneous fleet of vehicles with different capacities for dry or refrigerated cargo (\(CT_{k,t}\)).  
Dry cargo vehicles transport various products with less stringent conservation requirements but are still subject to quality and safety standards. Refrigerated transport, or refrigeration, is essential for preserving perishable products sensitive to ambient temperature. Vehicles equipped with refrigeration systems prevent teh spoilage of food in transit. We consider the following four types of vehicles in the case study:  
* Type 1: Refrigerated van.
* Type 2: Refrigerated truck.
* Type 3: Dry cargo van.
* Type 4: Electric forklifts.  
Each factory, in each period, has a specific number of vehicles available for use, contracted to meet the demands; see Table 3. The choice of vehicles respects the specificity of the transported product.  
The vehicle is available for the factory when it is empty and not in transport. The vehicle is in use by the factory when it is attending to the operation of product transport, that is, a displacement between the origin factory (\(f\)) and the destination factory (\(f^{\prime}\)). As soon as the vehicle finishes its task, it is available at the factory of origin.
## 3 Materials and Methods
### Chemical Analysis of Juice, Condensate and Wine Samples  
Alcohol, pH, titratable acidity (TA, as g/L of tartaric acid) and volatile acidity (VA, as g/L of acetic acid) were measured using a Foss WineScan analyzer (Mulgrave, VIC, Australia), while red wine color and total phenolics were measured using the modified Somers method and methyl cellulose precipitable tannin assay [51], respectively. These measurements were completed by the Australian Wine Research Institute's (AWRI) Commercial Services Laboratory (Adelaide, SA, Australia). Residual sugar and malic acid were measured enzymatically (Boehringer-Mannheim, R-BioPharm, Darmstadt, Germany) with a liquid handling robot (CAS-3800, Corbett Robotics, Eight Mile Plain, Qld, Australia) and spectrophotometric plate reader (Infinite 200 Pro, Tecan, Mannedorf, Switzerland). The AWRI's Commercial Services laboratory also quantified volatile phenols and their glycoconjugates (in juice, condensate and wine) by GC-MS and HPLC-MS/MS, respectively; in each case using previously published stable isotope dilution assay methods [52; 53]. The preparation of internal standards, method validation and instrument operating conditions are detailed in these publications, but briefly: volatile phenols were quantified with an Agilent 6890 gas chromatograph coupled to a 5973 mass selective detector (Agilent Technologies, Forest Hill, Vic., Australia), using \(d_{3}\)-guaiacol, \(d_{3}\)-4-methylguaiacol, \(d_{7}\)-o-cesol, and \(d_{3}\)-syringol as internal standards (limits of detection were 1-2 ug/L); and volatile phenol glycoconjugates were quantified with an Agilent 1200 high performance liquid chromatograph equipped with a 1290 binary pump coupled to an AB SCIEX Triple QuadTM 4500 tandem mass spectrometer, with a Turbo VTM ion source (Framingham, MA, USA), using \(d_{3}\)-syringol gentiobioside (Toronto Research Chemicals, Toronto, ON, Canada) as an internal standard (the limit of detection was 1 ug/L).  
Qualitative analysis of condensate samples collected during SCC treatment of smoke-tainted wines was performed by Metabolomics Australia (AWRI, Adelaide, SA, Australia) using headspace-solid phase microextraction (HS-SPME) [54]. Briefly, samples (diluted to \(\sim\)1% abv, with water, and saturated with 2.0 g of sodium chloride) were extracted with a DVB/CAR/PDMS SPME fiber (Sigma Aldrich, North Ryde, NSW, Australia) for 15 min at 40 \({}^{\circ}\)C, prior to desorption (in spililtes mode), at an injector temperature of 260 \({}^{\circ}\)C, onto an Agilent 7890 A gas chromatograph fitted with an Agilent DB-624 UI column (30 m \(\times\) 0.25 mm \(\times\) 1.4 um) equipped with a Gerstel MPS2 multi-purpose autosampler, and coupled to a 5975C VL mass selective detector. Helium (ultra high purity) was used as the carrier gas in constant flow mode. GC-MS data from Agilent ChemStation software (v E.02.02) were exported for processing using R statistical programming software (v4.1.1, RStudio Inc., Boston, MA, USA). Volatile compounds were tentatively identified by comparing mass spectral data with the NIST mass spectral database.
## 4 Experiments, Results, and Analysis
### Small DNN  
Implementing a DNN with 992 hidden-layer neurons across four hidden layers poses significant computational training costs. To assess efficiency, its performance was compared with that of the S-DNN, with 100 neurons distributed over three hidden layers (50, 30, and 20 neurons, respectively). Despite using only a tenth of the neurons, the S-DNN achieved an \(R^{2}\) of 0.9270, which is 4.13% lower than that of the DNN.  
Figure 12 compares \(R^{2}\) performance of the S-DNN with the DNN as the number of component signals increases. The SNN has significantly lower accuracy estimating shape parameter \(a\) when there are fewer than six component signals. When seven or more component signals are used, the SNN has comparable performance to the DNN. This provides an alternative to using a model with considerably fewer parameters when an SDR is supporting a sufficiently large number of applications.  
Figure 11: Trends in \(R^{2}\) score as more component signals are added to the composite signal.  
Figure 10: Trends in MAE as more component signals are added to the composite signal.
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 5 Results and Discussion
### Metamaterial Arrays Configurations
#### 5.2.2 \(2\times 2\) Array Analysis  
For investigative purposes, the \(2\times 2\) array configuration was rotated 90 degrees, as seen in Figure 8a. The same methodology was applied to obtain the effective medium parameters from the arrays structure at 0-degree and 90-degree rotations. The real magnitudes of permeability and permittivity of the \(2\times 2\) array are revealed in Figure 8b,c. Figure 8b's permeability curve displays a resonance from 7.892 GHz to 14 GHz, which contains a bandwidth of more than 6 GHz, for 0-degree rotation and from 7.94 GHz to 14 GHz, also covering more than 6 GHz, for 90-degree rotation. From Figure 8c, it is evident that the permittivity curves exhibit a negative magnitude from 3.86 to 7.796 GHz, 8.036 to 8.708 GHz, 9.62 to 10.448 GHz, and 11.528 to 11.708 GHz for 0-degree rotation and from 4.22 to 7.928 GHz, 8.312 to 8.864 GHz, 9.836 to 10.628 GHz, and 11.336 to 11.648 GHz for 90-degree rotation. However, two bandwidths, 3.936 GHz for 0-degree rotation and 3.708 GHz for 90-degree rotation, are worth mentioning in the permittivity curve. It was mentioned earlier in this paper that a material's refractive index curve would be negative if its permittivity and permeability appeared negative simultaneously.  
In Figure 8d, the material shows negative peaks from frequencies of 3.656 to 6.176 GHz, 6.668 to 7.628 GHz, 7.856 to 10.544 GHz, 10.772 to 12.08 GHz, and 13.40 to 14 GHz that respectively cover bandwidths of 2.52 GHz, 0.96 GHz, 2.688 GHz, 1.308 GHz, and 0.60 GHz in the microwave regime at 0-degree rotation. The same applies for the frequencies of 3.752 to 6.344 GHz, 6.668 to 7.82 GHz, 7.94 to 11.84 GHz, and 12.356 to 14 GHz that respectively cover 2.592 GHz, 1.152 GHz, 3.90 GHz, and 1.644 GHz bandwidths in the microwave region for 90-degree rotation of the array configuration. Moreover, the \(2\times 2\) array structure has characteristics of a double-negative metamaterial at 8.60 GHz for both 0-degree and 90-degree rotation.  
Figure 8: _Cont._  
Figure 7: (**a**) 0-degree position of the \(1\times 1\) array geometry; real magnitude of (**b**) the effective permeability; (**c**) the effective permittivity; and (**d**) the effective refractive index at 0-degree and 90-degree rotations of the \(1\times 1\) array configuration.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 4 Results and Comparisons
### Significance of This Work  
The implementation results reported for our proposed \(\frac{throughput}{area}\) architecture on different 7-series FPGA devices reveal the suitability of this work in applications that demand key authentication before starting communications. The typical examples include the IoTs, health-related applications, smart cards, automotive mobile/vehicles, etc. More precisely, the IoT nodes require key authentication prior to starting communications [7; 8; 10]. Moreover, in modern health care systems [1], authentication is needed to retrieve data securely on an unsecured cloud. The V2X needs authentication to start secure communication over the network/cloud [2]. Authentication is needed to make online payments in the case of smart cards. The identification of a human using his/her smart ID card is another application where authentication is essential. Based on [43; 44], the aforementioned applications require  
Figure 5: Comparison with [35] in terms of throughput/slices on Virtex-7 FPGA.  
low-power for data transmission and authentication purposes. We believe the reported values for high throughput and low area, in this work, result in low power. Therefore, our proposed design could be beneficial for secure communication in IoT-related applications, where both throughput and area parameters are desired for cryptographic computations.
## 5 Experiments
### Test Results
#### 5.3.4 Property 4  
A node \(v_{i}\) with a lower metric than \(v_{j}\) will not forward DATA packets to \(v_{j}\). We model this property as follows:  
\[\forall_{x}(request(x)\wedge x.type=\textbf{DATA}\wedge i.metric>j.metric)\]  
Based on the above-mentioned collected traces, we continue to test Property 4, and the results are illustrated in Tables 6 and 7, Figures 6 and 7.  
Figure 4: Test results for Property 3 (Day 1).  
Figure 5: Test results for Property 3 (Day 2).  
During all of our testing process for this property on Day 1, no 'fail' verdicts have been obtained. Therefore, in order to better demonstrate that our approach and tool may detect different failures, we manually introduced some errors into several traces that will be tested on Day 2.  
Three types of errors were inserted into Traces 101, 105 and 108: wrong control parts; false payload (data part or erroneous timestamps); and we moved or deleted some of the packets. As shown in Table 7, all of the errors were successfully detected. We may however note that some of the removed packets generated some 'inconclusive' verdicts, which is not a bad or faulty result, since this could be a real effect of packet losses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traces** & **Total Messages** & **Pass** & **Fail** & **Inconclusive** \\ \hline
101 & 67,409 & 22,446 & 0 & 0 \\
102 & 95,206 & 29,552 & 0 & 0 \\
103 & 81,709 & 32,243 & 0 & 0 \\
104 & 84,590 & 29,128 & 0 & 0 \\
105 & 69,274 & 23,981 & 0 & 0 \\
106 & 70,113 & 23,983 & 0 & 0 \\
107 & 79,807 & 26,717 & 0 & 0 \\
108 & 78,483 & 29,330 & 0 & 0 \\
109 & 87,196 & 26,117 & 0 & 0 \\
110 & 62,235 & 19,837 & 0 & 0 \\ Gateway 500 & 60,805 & 17,639 & 0 & 0 \\ Gateway 501 & 52,444 & 14,659 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results for Property 4 (Day 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traces** & **Total Messages** & **Pass** & **Fail** & **Inconclusive** \\ \hline
101 & 13,203 & 4228 & 1 & 0 \\
102 & 18,342 & 5496 & 0 & 0 \\
103 & 15,744 & 5939 & 0 & 0 \\
104 & 18,852 & 6685 & 0 & 0 \\
105 & 14,711 & 5809 & 1 & 0 \\
106 & 12,85
## 5 Experimental Results
### Verification of AES Encryption and Decryption  
Before conducting SCAs, we first verified the correctness of data encryption and decryption between the client side and server side. By the lightweight D-H protocol, an AES-encrypted channel was established between the Arduino UNO and the control computer by \(Key[0]\); this channel ensured the confidentiality and integrity of transmitted data. After successfully establishing the secure channel, encrypted test messages were transmitted to the server. During the transmission process, Wireshark was employed to monitor and analyze the encrypted message packets. Figures 10 and 11 illustrate the captured network traffic, confirming the successful delivery of encrypted packets from the IoT device to the server. The analysis verified key indicators such as packet size, transmission sequence number, and destination address, ensuring that packets were neither altered nor dropped during transit.  
Figure 11 highlights the numbering of each transmitted data packet. For instance, the packet labeled \"2100\" represents the 2100 th AES-128 encrypted message. A comparison of packets 2098, 2099, and 2100 reveals that the encryption key for the 2100 th packet was dynamically replaced with a new key: \"AC D1 A2 97 DA B1 83 D5 EF D2 FA C5 D9 80 D9\". This validates the functionality of the dynamic AES key replacement mechanism, demonstrating its role in enhancing transmission security by using a unique encryption key for each subsequent packet.  
Figure 9: Experimental environment setup.  
The figure further confirms that the dynamically replaced key was successfully used by the server to decrypt the encrypted packets. For example, the deccrypted content of Message Number 2100 matches the expected plaintext message, verifying the accuracy of the encryption and decryption processes on both the client and server sides. This successful decryption not only confirms the correctness of the encrypted packets but also demonstrates the mechanism's reliability and integrity throughout the transmission process.  
Additionally, Figure 11 demonstrates that the dynamic AES key replacement mechanism maintains the correct sequence and integrity of the transmitted packets. The proper order of packets (e.g., 2098, 2099, and 2100) and their successful decryption validate that no data were lost or corrupted during transit. The server consistently verifies and processes the packets, ensuring reliable and secure communication.  
In summary, the analysis presented in Figures 10 and 11 underscores the effectiveness of the proposed dynamic AES key replacement mechanism. The accurate decryption of  
Figure 11: Process of server side receiving encrypted data and decryption.  
Figure 10: Server-side secure channel setup and encrypted packets transmission.  
Message Number 2100 serves as strong evidence of the mechanism's capability to ensure data integrity, reliability, and security across the entire communication process.
## 3 Results and Discussion
### Design of Experiment (DOE)
#### 3.2.6 Optimization  
One of the main objectives of this study was to find the optimal values of each parameter for which high extraction and back-extraction efficiencies of Cr(VI) are obtained.  
From Figure 16, it can be seen that the extraction efficiency can reach a value of 93.03% when the values of the three parameters are as follows:  
* Cr(VI) concentration, 1.0 ppm;
* Temperature, 20 \({}^{\circ}\)C; and
* PVC/Aliquat-336 ratio, 1/0.5.  
Figure 15: Contour plots of the back-extraction efficiency.  
Figure 17 shows the optimization of the different factors influencing the back-extraction efficiency of Cr(VI) in the receptor phase.  
It can be deduced from these figures that the back-extraction efficiency of Cr(VI) in the receiving phase can reach a maximum value of 59.34% at the following parameter values:  
* NaCl concentration, 2 M;
* Temperature, 50 \({}^{\circ}\)C; and
* PVC/Aliquat-336 ratio, 1/1.  
Similar results were reported by [PERSON] et al. [45] in the extraction of Cr(VI) by emulsion liquid membranes by trioctyl amine using response surface methodology. A yield of 92.5% was achieved for the optimized conditions. In another comparative study between trioctyl amine and Aliquat-336 for Cr(VI) extraction by emulsion liquid membrane, [PERSON] et al. [46] reported a maximum extraction yield, under optimal conditions, of 89.2% and 96.15% for trioctyl amine TOA and Aliquat-336, respectively. The most influential parameters were agitation speed, the ratio of organic/aqueous phases, and concentration of the carriers. In the same system (ELM), [PERSON] et al. [47] demonstrated that the most influential parameters for Cr(VI) extraction by Cyanex 923 were concentration of the surfactant Span span and ratios of organic/internal or external phases. As reported by [PERSON] et al. [48], more than 95% of Cr(VI) was extracted by Aliquat-336 in sunflower oil as solvent in bulk liquid membranes system. The transport of Cr(VI) through the bulk liquid membrane was influenced by strip phase concentration, strip phase pH, and carrier concentration.  
Figure 16: Factor optimization for Cr(VI) extraction.  
Figure 17: Optimization of back-extraction efficiency.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 1 Introduction
Because PAIs have complex structures and noise interference, traditional convolutional neural networks often perform poorly in processing PAIs, in which it is difficult to accurately reconstruct and segment the fine structures, and may suffer from overfitting or underfitting problems, resulting in insufficient model generalization ability [29]. Second, the application breadth and performance of deep-learning methods are limited because deep-learning models need a significant amount of labelled data for training, and the labelled data of PAI are hard to collect. In order to solve the above problems, this paper proposes a deep-learning PAI reconstruction and segmentation method based on the improved network enhanced deep super-resolution minimalistic network (EDSR-M) and DeepLabV3+ [30]. As an advanced model for super-resolution reconstruction tasks, the EDSR [31] network can effectively improve the spatial resolution and detail information of the image. The improved network EDSR-M replaces the original upsampling layer with a convolution layer, which can effectively reduce the computational burden, simplify the network structure, reduce the risk of overfitting, and help learn image features better. The DeepLabV3 + network is an advanced model for semantic segmentation tasks that can segment different objects and organizations in the image into semantic regions. These two network structures are able to make full use of the spatial and semantic information of the image in the PAI reconstruction and segmentation task, thus achieving more accurate and robust reconstruction and segmentation results. Experimental results show that the proposed method achieves significant improvements in the reconstruction and segmentation tasks of PAI, demonstrating the potential and effectiveness of deep learning in the field of PAI. This research result provides an important reference and support for further promoting the development of PAI technology in clinical applications.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 5 Comparative Analysis
Overall, the comparison highlights significant variability in hardware efficiency among modern lightweight block ciphers, underscoring the importance of careful selection based on implementation requirements. A distribution of lightweight block ciphers categorized by their gate equivalents (GEs), a measure of hardware implementation cost, as Figure 46 presents. The majority of ciphers fall within the 1001-1500 GE and 1501-2000 GE ranges, indicating moderate resource demands. A smaller group of ciphers achieves very low GE values (\(\leq\)1000), which are ideal for ultra-constrained devices. A few ciphers require over 2000 GE, reflecting higher complexity. This distribution highlights the diversity in hardware efficiency across modern lightweight cipher designs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Cipher** & **Block Size** & **Key Size** & **Structure** & **Rounds** & **GE** & **Limitation** \\ \hline
52 & SIMECK-T & 64 & 128 & Hybrid & 15 & 1350 & Narrow security margin. \\ \hline
53 & SFN & 64 & 96 & Hybrid & 20 & 1876 & High resource use. \\ \hline
54 & NDN & 64 & 80/128 & Hybrid & 12/18 & 1068/1272 & Needs detailed third-party cryptanalysis \\ \hline
55 & RAZOR & 64 & 128 & Hybrid & \(\geq\)10 & None & Limited hardware efficiency; untested beyond 15 rounds \\ \hline
56 & SPISE & 256 & 258 & Hybrid & 12 & 728 & Non-traditional S-box-free; large block size \\ \hline
57 & GFSPX & 64 & 128 & Hybrid & 20 & 1715 & Complex structure; key schedule overhead \\ \hline
58 & QLW & 64 & 128 & Hybrid & 19 & 1655 & No external validation; side-channel resistance untested \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 45: Gate equivalents (GEs) of LWBC.
## 2 Materials and Methods
### Intervention Method  
Group 1 patients learned active ankle pumping exercise for the operative limb and practiced regularly under the supervision of the nursing staff in the hospital. They were instructed to do the same at home after being discharged (Figure 1). The ankle pumping exercise consisted of rotating the ankle in a circle for 2 min, 20\({}^{\circ}\) dorsiflexion for 3 min, and 40\({}^{\circ}\) plantar flexion for 3 min in sequence. The ankle movements were performed at a frequency of once per second and at a cycle of 8 min, 1 cycle every 30 min, and 24 cycles per day [13]. Patients can execute the exercise in the supine, long sitting, sitting, and standing positions. An assistive device, such as a walker or cane, should be used if needed. In group 2, intermittent pneumatic compression was applied to the operative low limb during their stay in the hospital. The intermittent pneumatic compression used was an Active Compression System (Flowtron ACS900, ArjoHuntleigh Polska Sp. z o.o., Komorniki, Poland). The frequency of the intermittent pneumatic compression was set to once/min, and the duration was 16 h per day. The maximum pressure was set to 45 mmHg. They were also taught and instructed to do an active ankle pumping exercise, as those for group 1, after discharge. In group 3, in addition to active ankle pumping exercises for the operative limb, a foot band containing a reminder device and a strap (Figure 2) was used to remind the patients to perform the exercise. The reminder device, which was firmly attached to the strap, was worn around the wrist or midfoot while resting. Using a vibration alarm, the device reminded patients to exercise every 30 min. At the set time points, the vibration alarm started, and the patients had to put the device on their foot with the band. The sensor inside the device autodetected the movement of the ankle joint. The alarm stopped if an adequate number of ankle movements of more than 40 degrees were performed. The alarm started again at the next time point. Patients and their caregivers were taught about its usage during the hospital stay and instructed to continue using it at home after discharge. The band on the midfoot and above the slipper's upper vamp does not hinder in-door walking. The foot band was used throughout the day except during sleeping hours. The foot band was removed on the follow-up visit of the patient after fourteen days. The only difference between groups 1 and 3 was the use of a reminding vibration alarm for exercise during the hospital stay and after discharge for group 3. The intervention was performed in the operative limb, while the nonoperative limb did not undergo intervention.