esa-sceva/satcom-chunk-collection huggingface dataset:
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method
### Block-Based Pixel Modification  
[PERSON] et al. performed the modification of the pixel value by randomly selecting the pixel position, the defect of which easily causes very limited pixel modifications in certain smooth areas. This also makes the comparison between the modified block and the surrounding unmodified blocks quite obvious; in other words, the visual quality with their method will be decreased (block effect). In order to avoid this, [PERSON] et al. [14] initially proposed the block-based method after learning the human visual system (HVS) [19] and applying [PERSON] et al.'s idea. In this method, the average variance of each sub-block is calculated to reflect its smoothness. When one current pixel is modified, it is sorted according to the mean squared error (MSE) of the block, and then, the non-smooth region will be preferentially modified according to the feature of the current block. Given an image \(I\) with the size of \(M_{0}\times N_{0}\), if the block size in the image is set as \(n\times n\), there will exist \(\frac{M_{0}}{n}\times\frac{N_{0}}{n}\) blocks. The mean and mean squared error of these blocks are calculated, respectively, as shown in Equations (5) and (6).  
\[mean(p,q)=\frac{1}{n\times n}\sum_{i=1}^{n}\sum_{j=1}^{n}I(i,j) \tag{5}\]  
\[MSE(p,q)=\sqrt{\frac{1}{n\times n}\sum_{i=1}^{n}\sum_{j=1}^{n}(I(i,j)-mean(p, q))^{2}} \tag{6}\]  
where \(mean(p,q)\) and \(MSE(p,q)\) are the two functions calculating the average value and mean squared error located at the block \((p,q)\), respectively, and both \(p\) and \(q\) satisfy the conditions \(p=1,2,\cdots,\frac{M_{0}}{n},q=1,2,\cdots,\frac{N_{0}}{n}\).
###### Abstract  
Urban road networks are prone to disruptions that can result in localized congestion or even complete interruptions, thereby causing delays in conventional logistics distribution. To mitigate this issue, the present study proposes a dynamic deployment model and task planning methodology for vehicle-drone collaborative delivery in areas affected by road disruptions. Utilizing complex network theory, a framework for identifying node vulnerabilities within road networks is established. Furthermore, a dynamic model for selecting drone take-off and landing sites, as well as task planning, is developed with the dual objectives of minimizing delivery costs and time while maximizing demand coverage. An enhanced evolutionary algorithm is devised to address the model. Results from case studies indicate that when the failure rate of regional road network nodes reaches 50%, the network vulnerability value is 0.8, achieving an air-ground collaborative logistics task completion rate of 95% and a delivery time of approximately 120 min. Conversely, when node failure escalates to 70%, the vulnerability value approaches 1.0, while still achieving a 90% task completion rate and a delivery time of 150 min. The proposed air-ground collaborative dynamic logistics approach effectively addresses distribution challenges in disrupted road networks and offers technical support for the advancement of urban low-altitude logistics.  
complex network; vulnerability; air-ground collaboration; improved evolutionary algorithm 2020
# A Reversed-Phase HPLC Method for Determination of Osteopontin in Infant Formula
## 1 Introduction  
Osteopontin (OPN) is a highly phosphorylated acidic glycoprotein and composed of \(\sim\)300 amino acid residues [1]. [PERSON] et al. [2] reported that it is present in human milk (138 mg/L) at levels much higher than in both infant formula (\(\sim\)9 mg/L) and bovine milk (\(\sim\)18 mg/L). It is a multifunctional whey protein with various biological, physiological and pathophysiological activities [3, 4]. OPN also facilitates functions of lactoferrin, another bioactive protein [5]. Therefore, recently, OPN is being considered as a potential candidate protein to fortify infant milk formula (IMF), having proven benefits after clinical trials [3]. OPN is extracted commercially from bovine milk using ion-exchange chromatography with quality sufficient to add into food products [6].  
OPN can be measured by enzyme-linked immunosorbent assay (ELISA); however, the method is very sensitive, time-consuming and costly. Although ELISA is a well-established method; in industrial contexts, continually improved high-performance liquid chromatography (HPLC) is preferred and adopted due to its better specificity, range of analytes, speed, throughput and multiplexing capacities resulting in reduced sample size and low cost per test [7, 8]. Moreover, HPLC instruments are commonly available in any testing facility. Therefore, an easy and cost-effective method is required to determine OPN content for routine use in food and dairy industry. In particular, reversed-phase high-performance liquid chromatography (RP-HPLC) is a common analytical method that has been widely used. Previously, liquid chromatography and mass spectrometry were applied to measure human OPN only as a biomarker [9], for identification and characterization of bovine OPN fragments [10, 11] and to separate N- and C-terminal in investigating the binding of human OPN to integrin [12, 13] as summarized in Table 1 (Section 3.1).  
However, there is no RP-HPLC method available in the literature to measure bovine OPN quantitatively, which has led us to perform this investigation.  
The aim of this work was to develop and validate a liquid chromatographic method to determine OPN content in IMF.
## 4 Volatile Aroma Compounds Biosynthetic Pathways and Related Enzymes
### Amino Acid Pathway  
Amino acid, such as alanine, valine, leucine, isoleucine, phenylalanine and aspartic acid, are also involved in aroma biosynthesis in fruit as direct precursors, and their metabolism is responsible for the production of a broad number of compounds, including alcohols, carbonyls, acids and esters [4,143]. Most of the information available to date on the biosynthesis of amino acid-derived volatiles in plants is based on precursor feeding experiments with radio-labeled, stable-isotope-labeled, or unlabeled precursors. The general scheme of biosynthesis is thought to proceed in a similar way as that in bacteria or yeast, where these pathways have been studied more extensively [144,145]. Amino acids can undergo an initial deamination or transamination leading to the formation of the corresponding\(a\)-keto acid (Scheme 2). Subsequent decarboxylation followed by reductions, oxidations and/or esterifications give rise to aldehydes, acids, alcohols and esters [16]. Branched chain volatile alcohols, aldehydes and esters in fruits such as banana, apple, strawberry and tomato arise from the branched chain amino acids leucine, isoleucine and valine [10,146-148]. These amino acids can also be the precursors of acyl-CoAs, which are used in alcohol esterification reactions catalyzed by AATs. Indeed, isoleucine could give rise to 3-methylbutanol and 2-methylbutyl-CoA, both used in an esterification reaction to yield the ester 3-methylbutyl 2-methylbutanoate in banana [149]. Methionine could be the precursor of sulphur-containing volatiles such as dimethyldisulfide and volatile thioesters [18]. In strawberry, it has been suggested that alanine serves as a precursor for volatile ethyl esters, which can be produced by AAT [149,150].  
**Scheme 1.** Linolenic acid-derived flavor molecules. AAT, alcohol acyl CoA transferase; ADH, alcohol dehydrogenase; AER, alkenal oxidoreductase; AOC, allene oxide cyclase; AOS, allene oxide synthase; HPL, hydroperoxide lyase; JMT, jasmonate methyltransferase; LOX, lipoxygenase; OPR, 12-oxo-phytodienoic acid reductase; 3\(Z\),2_E_-EI, 3\(Z\),2_E_-enal isomerase.
## 5 Reviewed Papers on Empirical-Based Path Loss Models
### Performance Evaluation of Empirical Path Loss Models in Indoor Environment  
Considering the deployment of empirical models for path loss prediction in indoor environments in the mid-band frequency spectrum, quite a lot of the proposed models have outperformed the conventional ones, like in the case of [70], where the proposed model outperformed the log-distance model with the minimum value of RMSE at 2.1 GHz. On the contrary, the ITU-R model was overestimated at 3.5 GHz in [61] with a MAPE value of 10.71, while FSPL was underestimated with a MAPE value of 4.83, as shown in Figure 5.  
On the high-band spectrum, CI and FI models performed excellently well in the work of [48] at 14, 18, and 22 GHz, and [71] at 28 GHz. Both models provided accurate estimates that fit the real measured data, with CI having the lowest RMSE value of 2.5 dB in [48] and 3.8 dB in [71].  
Figure 4: Performance analysis of empirical path loss models in mid-band and high-band frequencies within urban environments.  
Figure 5: Comparative assessment of empirical path loss models in mid-band and high-band frequencies for indoor environments.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation
### Complete Threshold Voltage Model  
Based on the analysis of threshold voltage in long channel devices, the influences of substrate bias, short-channel effect, drain-induced barrier lowering effect, and narrow-channel effect on threshold voltage were studied. Considering all the factors related to threshold voltage, the complete threshold voltage of a strained Si NMOSFET is determined as:  
\[V_{TH}=V_{THL}+\Delta V_{th}(SCE)+\Delta V_{th}(DIBL)+\Delta V_{th,W}, \tag{32}\]  
Among them, the expressions for \(V_{THL}\), \(\Delta V_{th}(SCE)\), \(\Delta V_{th}(DIBL)\), and \(\Delta V_{th,W}\) are given by Equations (22) and (29)-(31), respectively. By substituting these expressions into Equation (32), we can obtain the complete expression for the threshold voltage of a strained Si NMOSFET as:  
\[\begin{split}& V_{TH}=V_{TH0}+K_{1}\big{(}\sqrt{\phi_{s}-V_{bs}}- \sqrt{\phi_{s}}\big{)}+K_{2}V_{bs}+(K_{3}+K_{4}V_{bs})\frac{T_{ox}}{W_{eff}} \phi_{s}\\ &-0.5\frac{K_{8}}{\cosh\left(K_{6}\frac{t_{eff}}{t_{f}}\right)-1 }(V_{hi}-\phi_{s}+V_{1})-\frac{0.5}{\cosh\left(K_{7}\frac{t_{eff}}{t_{0}} \right)-1}\times(K_{8}+K_{9}\cdot V_{bs})\cdot V_{ds}.\end{split} \tag{33}\]
## 4 Experimental Results and Discussion  
In our experiment, the lasing threshold of the microchip laser was found at around 900 mW, at room temperature. At first, it was a single frequency output with x polarization. And when the pump power was increased, the y polarization TEM\({}_{00}\) mode was generated, due to a relatively low spectral gain for the y polarization mode. If the pump power increased to exceed 1.5 W, the neighboring longitudinal mode was generated. In the experiment, we obtained a dual-frequency laser output of about 34 mW at a pump power of 1.5 W.
## 5 Calculation Results and Discussion  
### Correlation of Braking Torque and Rotation Speed  
The braking torque at the ECB is strongly influenced by the skin effect, which is also increased by the increase in rotation speed. This is indicated by the tendency of the stronger generated magnetic field, resulting in the changes in flux density. The skin effect can be described as the phenomenon of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rotation Speed (rpm)**} & \multicolumn{2}{c}{**Torque (Nm)**} & \multirow{2}{*}{**Error Difference \%**} \\ \cline{2-3} \cline{5-5}  & **Experiment** & & **FEM** \\ \hline
150 & 7.3 & 6.97 & 4.52 \\
300 & 10.3 & 10.92 & 6.01 \\
450 & 11.5 & 12.03 & 4.6 \\
600 & 12 & 11.67 & 2.75 \\
750 & 11.2 & 10.69 & 4.5 \\  & Average error (\%) & & 4.476 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental data and numerical calculation conducted based on the finite element method (FEM).  
Figure 6: Correlation between the rotation speed and torque during the validation test.  
electron accumulation in a conductor, which is concentrated in the conductor's skin. The presence of a skin effect results in an increase in current density in the farthest part of the conductor core, as shown in Figure 7.  
From Figure 8, it is clear that a higher rotation speed results in an increased skin effect. This skin effect causes a decrease in the braking torque at high speed. When the rotation speed is 150 rpm, the magnetic field generated on the disk is still in the area close to the source of the magnetic field. However, as the rotation speed is increased to 450 and 750 rpm, the generated magnetic fields are distributed at the area outside of the magnetic field source. It seems that the rotation speed of 450 rpm is the critical speed in this developed design. The secondary magnetic field for this rotation speed is higher than the one of lower rotation speed but still centralized near to the magnetic field source. Meanwhile, at a rotation speed of 750 rpm, the braking torque begins to decrease due to the distribution of the secondary magnetic field.  
Figure 8: Effects of the number of coil windings on the braking torque.  
Figure 7: Magnetic flux density change at different rotation speeds: (**a**) 150 rpm; (**b**) 450 rpm; (**c**) 750 rpm.
# Bluetooth Low Energy Mesh Networks: A Survey
## 4 BLE Mesh Networks: Academic Solutions
### Alber  
Similarly to BMN, another solution based on a DAG structure (in this case created by the RPL protocol), called Adaptation Layer between BLE and RPL (ALBER), has been proposed over Bluetooth 4.1 [18]. ALBER performs four different tasks: (a) broadcasting RPL control messages through BLE advertising channels; (b) broadcasting RPL routing metrics values that reflect BLE link qualities; (c) transmitting routing table updates; and (d) managing parent changes in order to prevent packet loss.  
In order to determine the Rank of a node, in this solution nodes use a metric inspired by ETX. Since the Link Layer in BLE does not provide retransmission information to upper layers, ALBER requires the L2 CAP layer to transmit ring packets to parent nodes and uses the obtained Round-Trip Time (RTT) measurements to calculate the Rank value for this node. The authors found experimentally that RTT in BLE can be computed in terms of _connInterval_ periods. Thus, the authors defined a new metric called Expected number of Connection Intervals (ECI), which reflects RTT expressed in terms of _connInterval_ periods.  
The authors performed an experimental evaluation of their solution, focusing on three main objectives: (i) comparing RPL over BLE with RPL over IEEE 802.15.4; (ii) determining the impact of the _connInterval_ setting; and (iii) evaluating the effect of ECI on the performance of RPL over BLE. Regarding the first objective, the authors obtained better PDR with RPL over BLE than over IEEE 802.15.4, which they attributed to the adaptive frequency hopping mechanism used in BLE. Moreover, spasmodic PDR degradation in IEEE 802.15.4 caused frequent parent changes. In relation to the second objective, the authors found that PDR performance was improved by reducing _connInterval_. However, increasing _connInterval_ decreases energy consumption. Finally, the authors found that using ECI reduces the amount of parent changes and improves PDR.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method
### Anomaly Detection  
For the given set of multi-source behavior sequences \(S^{\prime}\), the deep clustering network outputs the class labels and class assignment probabilities for the user behavior event sequences in the set \(S^{\prime}\). Based on the clustering results, we consider two types of anomalies: cluster anomalies and outlier anomalies.  
Assuming that the behavior event sequences in the user's multi-source behavior sequence collection \(S^{\prime}\) are clustered into \(k\) classes. To begin, calculate the standard deviation of the number of behavior event sequences for each class label:  
\[\sigma=\sqrt{\frac{1}{k}\frac{k}{j=1}\big{(}x_{j}-\mu\big{)}^{2}} \tag{7}\]  
In the given context, \(x_{j}\) represents the number of behavior event sequences for the \(j\)-th class label, while \(\mu\) represents the average number of behavior event sequences for all class labels.  
The standard deviation of the quantity of behavior event sequences for class labels reflects the balance of the distribution of behavior sequences in the clustering results. A larger standard deviation indicates an imbalanced distribution of behavior sequences in the clustering results. In this case, clusters with fewer behavior sequences are identified as anomaly clusters, and all behavior sequences within those clusters are considered anomalies. Conversely, a smaller standard deviation indicates a balanced distribution of behavior sequences in the clustering results. In this scenario, if there are behavior sequences with low class assignment probabilities to their corresponding clusters, those sequences are classified as outliers.
## 8 Limitations of Research  
Although this SLR has been conducted following established guidelines [12] and strictly adhering to the developed review protocol, certain limitations remain:  
* **Search Exhaustiveness:** We have selected precise search terms and screened results thoroughly. However, some queries returned thousands of articles, making full review difficult. Several studies were excluded based on titles alone, which may have led to missed relevant work. Therefore, this review does not claim complete exhaustiveness.
* **Database Selection:** This SLR uses four major databases: IEEE, Elsevier, Springer, and Google Scholar. These sources ensure access to high-quality journals. However, some relevant studies from other databases may be missing. This could affect coverage of very recent research. Still, the selected databases offer a strong and representative view of current UWA communication advances.
* **Scope of Inclusion Criteria:** The inclusion criteria were designed to focus on studies published between 2020 and 2025. While this ensures relevance to recent advancements, older foundational works that may still hold significance were excluded. Future research could incorporate a broader time frame to capture historical developments in machine/deep learning applications for underwater acoustic communication.
* **Generalization of Findings:** The findings of this SLR are based on selected studies that met predefined criteria. While efforts were made to ensure a balanced representation of methodologies, there is a possibility that certain niche approaches or emerging techniques were underrepresented. Further studies incorporating additional perspectives may enhance the comprehensiveness of future reviews.  
Despite these limitations, we believe that the core findings of this SLR provide valuable insights into the role of machine/deep learning in underwater acoustic communication and serve as a strong foundation for future research.
# Control of Ribosome Synthesis During the Cell Division Cycles of _E. coli_ and _Synechococcus_
###### Abstract  
The regulation of ribosome synthesis has been investigated for nearly five decades. In earlier studies, the control of rRNA synthesis in bacteria was found to be dependent on nutrient composition of the growth media or cell growth rates, and these observations led to the growth rate-dependent regulation model. Also developed were stringent control, feedback ribosome synthesis, passive regulation, and antitermination models. Current evidence indicates that upstream (UP) element, molecular effectors, ppGpp and INTP (initiating nucleoside triphosphate), and trans-acting proteins, Fis and H-NS, play important roles in the control of rRNA synthesis in response to changing nutritional environments. The mechanisms for the ribosome feedback regulation, and growth rate-dependent controls of rRNA synthesis remain to be determined despite numerous investigations. r-protein synthesis can be controlled by translational coupling, translation repression, or premature transcription termination. In _Synechococcus_, a photoautotroph, ribosome synthesis occurs early in the cell cycle as programmed events under conditions that support balanced growth. Periods of r-protein synthesis occur before rRNA synthesis periods, and rRNA synthesis is stimulated by a light-activated gene regulatory protein. These observations suggest that gene regulatory proteins are involved in the coordinate regulation of ribosome assembly in _Synechococcus_.
## 5 Experimental Results
### Real Data in Point Set Form  
The results presented here were obtained by converting point set data into kNN graphs and clustering using NBR-Clust. Four real datasets from the UCI Machine Learning repository [62] are evaluated: iris, breast-WI, _E.Coli_ and wine. Percent accuracy for these datasets is shown in Table 6. VAT, integrity and tenacity generally perform well on these datasets. The matching 90% accuracy of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Average Degree**} \\ \hline
**Measure** & **40** & **30** & **20** & **15** & **10** \\ \hline VAT & 98 & 88 & 86 & 71 & **73** \\ integrity & **100** & **99** & **97** & **95** & **86** \\ toughness & **98** & **92** & **75** & **84** & **68** \\ tenacity & **98** & **92** & **75** & **85** & **68** \\ scattering & 67 & 62 & 46 & 30 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percent accuracy for attributed networks with communities. Bold text indicates one-step clustering.  
Figure 6: BTER Scale-Free network generated by FEASTPACK.  
VAT and integrity on the well-known iris dataset is particularly notable, as two of the three iris clusters are not linearly separable. For comparison to existing clustering methods (both point clustering and graph clustering), k-means clusters iris at 89%. The corresponding kNN graph is clustered by spectral clustering at 90% and by Girvan-Newman at 97%. Notably, integrity achieves a high accuracy on iris _in one shot_. Integrity and tenacity both clustered all four datasets in one shot.  
k-means, spectral clustering, and Girvan-Newman clustered the breast-WI dataset at 85%, 82%, and 77% accuracy, respectively; while the NBR-Clust accuracies ranged from 81% to 85%. For _E. coli_, the highest accuracy is integrity at 60%, and most measures cluster closer to 55% accuracy. k-means clusters the _E. coli_ dataset at 58% accuracy, spectral clustering at 50% and Girvan-Newman at 61% accuracy. On the wine dataset, NBR-Clust beat k-means and spectral, which clustered at 61% and 70% accuracy, and it tied with Girvan-Newman at 71% accuracy.
## 3 Experimental Results by Unit Testing
### Basic Telemmunication Test  
A basic telecommunication test was conducted in various places including indoor, hallway, and outdoor areas, as shown in Figure 5. The experiment examined whether multiple anchors installed in a fixed position of each place effectively monitor the coordinate values of immovable or moving tags. The experimental result showed that the packet with accurate position coordinate values is received normally.
## 8 Evaluation
### Baselines  
We compared our model against three baseline methods: OpenWhisk Default, Least Connection, and Hot Concurrency. A brief explanation of each method is as follows: _Hot Concurrency (HotC)_: Hot Concurrency refers to maintaining \"warm\" function instances to handle multiple concurrent requests [17], representing a proactive warm-container scheduling mechanism. _Least Connection (LC)_: directs new requests to the instance with the fewest active connections. In the context of FaaS, this method distributes invocations to the least busy containers [35], prioritizing load balancing in the scheduling decision. The _OpenWhisk default (OW)_: employs a pull-based model, where invokers retrieve tasks from a message queue as they become available, functioning as a reactive scheduling mechanism [9].