esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
###### Abstract  
Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation.  
underwater wireless sensor networks; cooperative communication; cooperative error control; cooperative routing 20242424248. [[https://doi.org/10.3390/s24134248](https://doi.org/10.3390/s24134248)]([https://doi.org/10.3390/s24134248](https://doi.org/10.3390/s24134248))
###### Abstract  
The intensity and frequency of harmful algal blooms (HABs) have increased, posing a threat to human seafood resources due to massive kills of cultured fish and toxin contamination of bivalves. In recent years, bacteria that inhibit the growth of HAB species were found to be densely populated on the biofilms of some macroalgal species, indicating the possible biological control of HABs by the artificial introduction of macroalgal beds. In this study, an artificially created _Ulva pertusa_ bed using mobile floating cages and a natural macroalgal bed were studied to elucidate the distribution of algal growth-limiting bacteria (GLB). The density of GLB affecting fish-killing rapidophyte _Chattonella antiqua_, and two harmful dinoflagellates, were detected between \(10^{6}\) and \(10^{7}\) CFU g\({}^{-1}\) wet weight on the biofilm of artificially introduced _U. pertusa_ and \(10\) to \(10^{2}\) CFU mL\({}^{-1}\) from adjacent seawater; however, GLB found from natural macroalgal species targeted all tested HAB species (five species), ranging between \(10^{5}\) and \(10^{6}\) CFU g\({}^{-1}\) wet weight in density. These findings provide new ecological insights of GLB at macroalgal beds, and concurrently demonstrate the possible biological control of HABs by artificially introduced _Ulva_ beds.  
harmful algal bloom; mitigation; macroalgal bed; growth-limiting bacteria
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
## 2 Methodology
### Testbed Setup  
#### 2.3.1 Retro-VLC Link  
The Retro-VLC testbed (Figure 5) includes a Retro-VLC tag with a microcontroller unit (MCU) and a pixelated liquid crystal display (LCD) shutter, a VLC AP with LEDs and an embedded PD, and a SDS2104X plus digital oscilloscope (sourced from SIGLENT Technologies, Solon, OH 44139, USA) for displaying and recording the data output from the VLC AP.  
On the Retro-VLC tag, the LCD shutter is a customized 6x6 graphic shutter from liquid crystal technologies [21]. The size of each individual shutter is 6 mm \(\times\) 6 mm. The LCD shutter covering a 3M retro-reflective tape [22] is mounted on a customized PCB board, which is used to wire the shutters to the I/O pins of the MCU. Each I/O pin runs a TTL logic signal to control its corresponding shutter with independent timing. A separated computer connected to the MCU is used to generate the QAM symbols. The symbol samples are sent over the serial port to the MCU. The MCU turns on and off I/O pins according to the received sample value. To avoid overflowing the serial port buffer, the computer sends new bytes only after receiving a request from the MCU.  
On the VLC AP, the embedded PD is a silicon switchable gain detector PD100A2 from Thorlabs [23]. The gain is set to 20 dB. The PD is mounted with a 25 mm focal length lens in a 1\({}^{\prime\prime}\) diameter and 15 mm long lens tube. There are a total of 16 LEDs powered by a 12 V DC voltage source running through a 4 \(\Omega\) resistor for current limiting. The output of the PD is fed directly into the oscilloscope with 2000 samples per second, which is much higher than the Nyquist rate. The recorded samples of the QAM signal are exported to Matlab for offline processing.  
Figure 5: Testbed setup of Retro-VLC link.  
In addition to the demodulation procedures discussed in Section 2.1, the continuous dynamic phase offset [24] manifests as the points on the constellation diagram keep moving along the unit circle in the same direction and finally form a circle. The continuous dynamic phase offset is compensated for by multiplying each sample by a factor of \(e^{\phi_{c}\times m}\), where \(m\) is an index of the sample being multiplied and \(\phi_{c}\) is the difference in phase between consecutive QAM symbols. Hard-decision decoding [12] is adopted to convert the I-Q values into the received sequence of bits.
# A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection
## 4 Experimental Evaluation
### The Experimental Performance
That is, the attacker will complete the attack task in the shortest possible time and try to behave normally the rest of the time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Detection Rate (\%)** & **Missing Rate (\%)** & **False Alarm Rate (\%)** \\ \hline N.bayes & 63.88\% & 32.12\% & 4.00\% \\ Uniqueness & 37.4\% & 60.3\% & 2.30\% \\ Hybrid Markov & 45.3\% & 40.44\% & 14.26\% \\ Bayes1-step Markov & 67.3\% & 30.8\% & 1.9\% \\ IPMA & 40.63\% & 42.37\% & 17\% \\ Sequence matching & 35.9\% & 60.2\% & 3.9\% \\ Compression & 29.7\% & 50.5\% & 19.8\% \\ Closeness & 75.2\% & 20.1\% & 4.7\% \\ Our algorithm & 84.61\% & 15.39\% & 4.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between different algorithms on UNM_sendmail. Figure 11: The DR comparation of different algorithms on UNM_live_lp. As shown in Figure 12, our algorithm has the highest comprehensive detection rate 86.92% among five algorithms. In all the algorithms, our algorithm has the highest detection rate, and the lowest false alarm rate and missing rate. The formula of comprehensive detection rate is closer to reality. The comprehensive detection rates of N.bayes, Uniqueness, Hybrid Markov, Bayes1-step Markov, IPMA, Sequence matching, Compression, Closeness, and our algorithm are 61.32%, 36.54%, 38.84%, 60.02%, 33.72%, 34.49%, 23.81%, 71.67%, and 80.79% respectively.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 4 Experiments  
Eight test images from the USC-SIPI image database, i.e., Lena, Goldhill, Airplane, Boat, Peppers, Tiffany, Man, and Sailboat, have been used as the original protected images as shown in Figure 6. They all have the size of \(512\times 512\). Two groups of experiments were performed to evaluate the performance of the proposed scheme, i.e., (1) performance analysis of the proposed scheme and (2) performance analysis against data-loss.  
The peak signal-to-noise-ratio (PSNR), the structural similarity index measurement (SSIM), and the embedding capacity (EC) have been considered as the three primary factors to be evaluated in our proposed scheme. The PSNR defined in Equation (10) and the SSIM in reference [30] were used to evaluate the quality of the image. The larger the PSNR value is, the better the quality of the image is.  
\[\left\{\begin{array}{l}PSNR=10\log_{10}\frac{255^{2}}{MSE}\\ MSE=\frac{1}{M\times N\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\left(D(x,y) \right)^{2}}\end{array}\right., \tag{10}\]  
where \(M\times N\) represents the size of the images, and \(D(\cdot)\) is a difference matrix of the pixel values between the original image and the recovered image. SSIM simulates the human visual system (HVS) to assess the quality of images by comparing their luminance, contrast, and structure. Generally, the closer the value of SSIM is to 1, the better the visual quality of the stego-image is.  
The embedding capacity in our scheme was expressed by bits and defined as follows:  
\[EC=\left[\beta\times B^{2}\right]\frac{M\times N}{B^{2}}(bits), \tag{11}\]  
where \(\beta\) is the compression ratio, \(B\) is the size of the block, and \([\cdot]\) denotes that the value will be rounded to the nearest integer. Usually, the embedding capacity is flexible and changeable depending on the compression ratio.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards
#### 2.1.6 ISO 20078 (ITS--Vehicular Communications (GeoNetworking))  
ISO 20078 acts as essential support for ITS by establishing protocols for GeoNetworking that enable accurate location-based exchanges in-vehicle networks. This standard aids essential applications including traffic guidance and safety service and advances the emergence of inventive ITS solutions centered on geographic information. By establishing a standardized approach to GeoNetworking, ISO 20078 ensures interoperability and advances the evolution of smarter and more responsive transportation ecosystems [48].
## 2 Materials and Methods
### In Vitro Release Experiments  
LP2 release from SLNs was determined using dialysis membranes (Spectra/PorTM CE, diameter 29 mm, Mol. Wt. cutoff: 3000, Spectrum, Los Angeles, CA, USA), previously moistened by immersion in water for 24 h at room temperature. The dialysis medium consisted of water/ethanol (80/20 \(v\)/\(v\)) to increase LP2 solubility in the receptor medium, thus ensuring pseudo-sink conditions during the experiments. The same receiving phase had already been used in previous studies to evaluate in vitro release from SLNs of lipophilic drugs and did not lead to any alteration of nanoparticle structural properties [15]. The dialysis medium was stirred (700 rpm) and thermostated at 37 \({}^{\circ}\)C during the experiment. A total of 1000 \(\mu\)L of each formulation was pipetted into a dialysis membrane bag that was sealed and immersed in a beaker containing 65 mL of the release medium. Unloaded SLNs were used as control. At intervals, samples (200 \(\mu\)L) of the release medium were withdrawn and replaced with an equal volume of receiving solution pre-equilibrated at 37 \({}^{\circ}\)C. Each experiment was run for 24 h and sheltered from light to prevent any photo-degradation. Each experiment was carried out in duplicate. The withdrawn samples were analyzed to determine LP2 content by the HPLC method developed in this study.
## 1 Introduction
The MPC models presented consider the power characteristics of wind farms and hybrid storage systems, and the results confirm the efficiency of the MPC model. The merit of propose method is as follows: First, two types of energy are adopted. Second, the MPC method is used to smooth wind power fluctuation. Finally, two type of MPC methods are discussed and compared. The contributions are listed as follows:  
1. We have adopted a fast response speed energy storage and a slow response speed energy storage to smooth the short term and the long term wind power fluctuations, respectively. Due to the two different response speeds, how to get the two energy storages to cooperate is a problem for real time scheduling. We have exploited the modularity of wind farms based on the MPC method with two types of storage to solve the problem. 2. We present that the MPC prediction horizon should be two hours according to the wind farm power spectrum density and prediction error. 3. Additionally, we have implemented a two-level MPC to reduce the optimal computation time. The experimental results are close to that of a single-level MPC. The single-level MPC has its own advantages in following the grid plan. The two-level MPC cannot be substituted for single-level MPC, and _vice versa_. 4. Two sound conclusions are drawn through theory analyses and simulations: One is that the decision values of the pumped storage are not sensitive to the flywheel capacity. The other is that in some situations, wind power generation being sent to the grid is sacrificed to reduce the wind curtailment. The rest of this paper is organized as follows. In Section 2, the problem to be addressed is described. In Section 3, the system configuration and operating process are given. In Section 4, the proposed system model is described. In Section 5, the numerical results are presented. Finally, the conclusions are presented in Section 6.
## 3 Materials and Methods
### Methods
#### 3.2.2 Low-Rank Adaptation  
Low-rank adaptation (LoRA) [35] is an advanced fine-tuning strategy specifically designed to efficiently optimize the parameters of large pre-trained language models. Instead of modifying all parameters, LoRA optimizes model weights by introducing low-rank matrices, which dramatically reduces the number of trainable parameters during fine-tuning. Compared to traditional full-parameter fine-tuning methods, LoRA not only lowers computational costs but also preserves the model's core parameters, thereby mitigating the risk of overfitting. Its ability to quickly adapt to new tasks while maintaining overall model stability makes LoRA particularly well-suited for domain-specific applications that require frequent adaptation.  
In traditional full-parameter fine-tuning, all model parameters must be updated for a new task. In contrast, LoRA significantly reduces the number of trainable parameters by decomposing the weight matrix into two low-rank matrices. LoRA introduces two low-rank matrices, \(A\) and \(B\), with smaller dimensions to approximate the original weight matrix. During fine-tuning, only these low-rank matrices are updated, leaving the original weight matrix unchanged.  
Assuming that the weight matrix of a large language model is \(W_{0}\), LoRA updates it in the following way:  
\[h=W_{0}x+Wx=W_{0}x+BAx \tag{1}\]  
where \(W=BA\). Due to the low rank of \(A\) and \(B\), the number of updated parameters is drastically reduced. In this way, the computational complexity and storage requirements of the model are also reduced.  
In this study, we applied LoRA to enhance the performance of large language models in plant protection question-answering tasks. Compared to traditional full-parameter fine-tuning, LoRA updates only the low-rank matrices of specific layers, reducing computational overhead while preserving the model's original knowledge structure. By applying LoRA to the InternLM-20B model, we improved its ability to model professional terminology and domain-specific knowledge in plant protection.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 4 Discussion
Specifically, the application of second derivative spectra combined with chemometric classification enabled the identification of unique discriminatory regions such as ~1040 cm\({}^{-1}\) (C-O stretching in lactose and lactic acid), 1240-1300 cm\({}^{-1}\) (Amide III and phosphate groups), and ~1745 cm\({}^{-1}\) (C=O stretching in lipids). These spectral markers, highlighted through VIP score analysis, provide enhanced molecular resolution compared to raw spectra and have not been systematically employed for Feta classification to date. Furthermore, the use of freeze-drying as a preparatory step significantly improved spectral clarity and model performance, with classification accuracy exceeding 95%. These findings demonstrate methodological advancements in both spectral treatment and sample preparation, positioning this approach as a robust framework for non-destructive authenticity assessment of high-value dairy products. While the findings of this study are encouraging, certain limitations should be acknowledged. The sample set, although representative, was limited in geographic diversity, which may affect broader generalizability. Additionally, external factors such as seasonal milk variation or specific processing differences were not systematically controlled. Nonetheless, the high classification accuracy across both freeze-dried and raw samples supports the robustness of the methodology. Future research should focus on expanding the dataset to include wider regional and seasonal variation, and explore the use of advanced machine learning techniques to further enhance model precision and adaptability for routine authenticity screening. While this study focused on milk species adulteration, other forms of economic adulteration remain a concern. These include the use of vegetable fats to replace milk fat, the incorporation of non-local or non-certified sheep's milk, and deviations from traditional maturation processes. Although not directly targeted in this study, the FTIR-based method is sensitive to changes in lipid and protein composition and may have broader utility in detecting such adulteration types. Future work could explore the method's applicability in identifying non-dairy fat adulteration or verifying PDO-compliant production practices.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 3 Ripening Is Influenced by Multiple Hormones and Light
### Influence of Light of Different Wavelengths on Ripening  
Plants have several photoreceptor proteins containing chromophores that sense different regions of the visible and UV spectrum, including the phytochromes (PHYs) that perceive red (R) and far-red light (FR) and measure the R/FR ratio, the cryptochromes (CRYs), phototropins and 'Zeitlupes' that sense blue/UV-A light and the UV-B receptor UVR8, which senses UV-B light via a cluster of tryptophan residues [183].  
The characterisation of the _high-pigment_ (_hp1_ and _hp2_) tomato mutants contributed to understand the role of light signalling during plant development and ripening. Ripe fruits of these mutants have higher levels of ascorbic acid (vitamin C), carotenoids, flavonoids andtocopherol (vitamin E) [184; 185; 186], and there is a general stimulatory effect of light on isoprenoid metabolism in fruit and vegetative tissues [187; 188]. These effects result from mutations in negative regulators of light signalling: _hp1_ is mutated in the nuclear protein UV-DAMAGED DNA BINDING PROTEIN1 (DDB1) and _hp2_ is mutated in a second nuclear protein, DEETIOLATED1 (DET1). The WT versions of these proteins are negative regulators of light signal transduction [185; 189; 190], and RNAi silencing of _SI-DDBI/HP1_ or _SI-DET1/HP2_ increased plastid biogenesis and carotenoid accumulation in tomato fruit [191; 192]. Silencing of three other tomato light signalling regulators (CUL4, COP1, PIF1a) also increased fruit carotenoid levels [185; 192; 193], and suppressing the light signalling hub gene _HY5_ produced an opposite phenotype. Recent work has shown that tomato _HY5_ (ELONGATED HYPOCOTYL5) regulates fruit ripening by targeting genes involved in carotenoid biosynthesis and ethylene signalling, and may also affect the translation efficiency of a set of ripening-related genes by targeting ribosomal protein genes [194].  
[PERSON] et al. [195] showed that the high carotenoid content in ripening _hp2_ fruits was associated with disturbed ethylene production, increased ethylene sensitivity and altered expression of several ripening TFs, including the downregulation of _EREE4_, a repressor of carotenoid synthesis, and altered auxin signalling. This was accompanied by severe downregulation of _AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA)_ genes and altered accumulation of _ARFs_ transcripts, with ARF2 proteins (ARF2a and ARF2b), which are involved in tomato fruit ripening.  
Thus, it is clear that there are strong interactions between light signalling, auxin and ethylene in tomato fruit, and that each make significant regulatory contributions to carotenoid biosynthesis and tomato fruit ripening [195]. Further work is required, however, before we can fully understand the molecular basis for the interactions between light, auxin and ethylene in the control of ripening.
# A Survey on Hardware Accelerators for Large Language Models
## 4 CPU- and GPU-Based Accelerators  
### SoftMax  
In 2022, [PERSON] et al. presented a novel framework for the acceleration of Transformer networks through recomposing softmax layers [19]. The softmax layer normalizes the elements of the attention matrix to values between 0 and 1. This operation was conducted along the row vector of the attention matrix. Based on the profiling, the softmax layer in the scaled dot-product attention (SDA) block used 36%, 18%, 40%, and 42% of the total execution times of BERT, GPT-Neo, BigBird, and Longformer, respectively.  
In their work, the authors proposed decomposing the softmax layer into sub-layers to match their data access patterns with adjacent layers. Then, by fusing the decomposed softmax sub-layers with the subsequent and preceding operations, they reduced the off-chip memory traffic and, thus, reduced the total execution time for this layer.  
Softmax recomposition achieved speedups of up to 1.25\(\times\) for BERT, 1.12\(\times\) for GPT-Neo, 1.57\(\times\) for BigBird, and 1.65\(\times\) for Longformer on an A100 GPU by significantly reducing off-chip memory traffic without any degradation in model accuracy.  
The reduction in off-chip memory traffic can also reduce energy consumption, although this was not measured in the specific paper.