esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digital Watermarking System for JPEGs
### Adaptive Threshold and Perceptual Mask  
The watermark strength adapts to local visual sensitivity, following the three-factor HVS model (luminance, contrast, texture) as validated in recent JND studies [69; 70]. We compute  
\[AT=AS\times JND,\qquad AS=\text{base\_strength}\times SF. \tag{3}\]  
Size factor. \[SF=\text{clip}\Big{(}\sqrt{w\,h}/100,\ SF_{\text{min}},SF_{\text{max}}\Big{)}\] (4)  
rescales the base strength for very small or very large images; clipping to \([1,5]\) follows the practice in modern perceptual metrics.  
Just-Noticeable Difference. \[JND=1+k_{L}L+k_{C}C+k_{T}T,\] (5)  
where \(L=\text{mean}(\text{block})\) (luminance masking), \(C=\text{std}(\text{block})\) (contrast masking), \(T=\text{estimate\_sigma}(\text{block})\) (texture masking).  
The coefficient values follow Weber-Fechner limits for near-threshold distortion:  
\[k_{L}=0.05\,SF,\qquad k_{C}=0.30\,SF,\qquad k_{T}=0.05\,SF. \tag{6}\]  
where \(k_{C}\) is larger because contrast masking dominates in the mid-high spatial frequencies used for embedding [71]. The perceptual mask modulates the embedding gains \(\alpha\) (DCT domain) and \(\gamma\) (DWT domain) to ensure the energy injection respects both global and local visibility limits.  
Psychovisual calibration.We confirmed the coefficients with a 14-subject ABX study (120 image pairs, \(\Delta E<1.5\) calibrated display). A three-up-two-down staircase located the JND; the chosen tuple \((k_{L},k_{C},k_{T})\) yielded the lowest false positive rate (3.1%) while keeping the mean PSNR above 48 dB. These parameters were therefore frozen for all subsequent experiments.
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 4 Cybersecurity Frameworks Used for Tools Classification
### Penetration Testing Execution Standard  
_PTES [9]_ is a standardised methodology for planning, executing, and reporting security tests and it is widely used within the cybersecurity industry as one of the most significant standards for conducting penetration tests. PTES was proposed by a group of penetration testers and security professionals to provide guidance and best practices for conducting effective penetration tests within legal and ethical boundaries. It consists of seven phases (Figure 3):  
1. _Pre-engagement Interactions_: In this phase, the scope and rules of engagement are defined through an agreement between the penetration testing team and the system's owner. The system's owner must provide permissions and authorisations, and communication lines must be established between the testers and the target organisation.
2. _Intelligence Gathering_: Information about the target organisation or system is collected using techniques such as open-source intelligence (OSINT) gathering, reconnaissance, and network scanning. Active and passive information-gathering methods are distinguished based on direct interaction with the target system.
3. _Threat Modelling_: This phase identifies potential vulnerabilities and threats specific to the target organisation or system. It involves analysing collected information, understanding infrastructure and architecture, prioritising attack vectors, and assigning risks to threats to inform vulnerability mitigation.
4. _Vulnerability Analysis_: Vulnerabilities and weaknesses in the target's systems and applications are identified and assessed, typically using classification systems like  
Figure 2: Classification criteria applied in this survey.  
the _Common Vulnerability Scoring System_ (CVSS). Manual and automated testing, configuration analysis, and examination of insecure application design may be involved.
* _Exploitation_: Vulnerabilities previously identified are exploited to compromise the target system, gain unauthorised access, or execute malicious activities. The goal is to demonstrate the impact of vulnerabilities and their potential exploitation, bypassing security mechanisms.
* _Post-Exploitation_: After successful exploitation, the focus shifts to determining the value of the compromised system, maintaining access, escalating privileges, and pivoting to other systems within the network. This simulates an attacker's post-compromise activities, considering the data's importance and the advantage provided for further attacks.
* _Reporting_: The final phase involves documenting the findings, including identified vulnerabilities, their impact, and recommendations for remediation. The report should be clear, concise, and actionable for the target organisation, tailored to various audiences ranging from senior managers to technical staff.  
The PTES methodology can be applied to various systems to assess their security, including networks and critical infrastructures [38].
## 3 Conceptual Design
### General Architecture
In this latter view, security is not seen as a standalone attribute, but rather as a combination of three attributes, namely confidentiality, integrity and availability. This is in line with the confidentiality, integrity and availability (CIA) triad model [49], which commonly acts as a fundamental guideline to help secure information systems by providing a measurement tool for security implementations. The following subsections will cover the dependability aspects of the IIP that relate to security and availability, including scalability.
## 2 Materials and Methods
### IMERG Data  
The Integrated Multi-satellite Retrievals for GPM (IMERG) product was created by intercalibrating, merging and interpolating all satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at finer scales from the TRMM and GPM eras over the entire globe. The IMERG data are produced by a Bayesian inverse algorithm applied to microwave brightness temperatures that is run several times for each observation time. IMERG can range from giving a quick precipitation estimate to research-level products as more data are used in the algorithm, including monthly gauges [12].  
The GPM core satellite can be considered an improved version of the TRMM satellite. It serves as both a calibration and an evaluation tool for all the passive microwave and infrared-based precipitation products integrated in IMERG. It has wider spatial coverage than TRMM, is sensitive to light precipitation, and it can also differentiate liquid and solid precipitation phases in terms of a probability for the presence of the liquid phase [13].  
This study used final half-hourly cumulative precipitation files at a resolution of 0.1 degree of latitude/longitude. Within these files, two variables were created: the multi-satellite precipitation  
Figure 1: The area of study is enclosed in the red polygon (**a**) and automatic meteorological stations shown in black dots (**b**). Note the irregularity shown in the topography (color in m). In yellow _SMW_ shows the location of the Sierra Madre Occidental, _SME_ stands for Sierra Madre Oriental and _Alt_ is the Mexican Altiplano.  
estimate, with gauge calibration in mm/h (millimeters per hour), and the probability of the liquid precipitation phase; the latter is not used in this research. Half-hourly IMERG version 3 files are available for the period from March 2014 until the present with a few months of lag time ([[https://storm.pps.eosdis.nasa.gov/storm/](https://storm.pps.eosdis.nasa.gov/storm/)]([https://storm.pps.eosdis.nasa.gov/storm/](https://storm.pps.eosdis.nasa.gov/storm/))). Since the March 2014 runs were suggested to be ignored by [PERSON] et al. [1], the study period was chosen to extend from 1 April 2014 to 31 October 2015.  
The precipitation estimates for precipitation data (\(P_{n}\), \(P_{n+1}\)) in two half-hour periods have been converted into hourly precipitation estimates (\(P_{hour}\)) following the mathematical relationship: \(P_{hour}(mm)=\left[P_{n}\left(\frac{mm}{h}\right)+P_{n+1}\left(\frac{mm}{h} \right)\right]*0.5(h)\). Daily and seasonal precipitation estimates were then created to be used in this research.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## 4 Conclusions  
The tests presented in this paper for WV-2 data show that RPFs are suitable for VHRSI orthorectification. Using the RPC file supplied by the provider, they can also be applied without GCPs, however the results present considerable positional errors, and few GCPs are sufficient to achieve good results. If RPCs are calculated by the user, several GCPs are necessary to limit residual values for Ground and Check Points.  
RPFs need 3D information: Planimetric coordinates are not sufficient for GCPs, and a DEM of the entire scene is indispensable to complete the orthorectification. However, Z accuracy can be lower than that of the X and Y coordinates. As a consequence, height values can also be extracted from the DEM if they are not available from topographic and GPS surveys. Detailed maps (scale 1:5000 or greater) as well as remotely sensed images with the same resolution (or better) of the panchromatic image--but already orthorectified--are suitable for extracting planimetric coordinates of GCPs and CPs.  
A low number of GCPs is sufficient to achieve a good performance with [PERSON]'s rigorous model.  
The results obtained with WV-2 showed the analogous impact of DEM by increasing the geometric resolution for Z extraction for both RPFs as well as [PERSON]'s rigorous model.  
Although PFs are inadequate for high-resolution satellite images, also when a large number of GCPs are used, they improve the results of RPFs if applied as a second step in the orthorectification process based on the combination of these algorithms.  
Future research will be aimed at a comparison of similar algorithms extended to other VHRSIs such as WorldView-3, and GeoEye-2.  
This paper brings together the results of studies performed within the project PRIN 2010-11 financed by MIUR (Ministero dell'Istruzione, dell'Universita e della Ricerca)--Italy and developed at the University of Naples \"Parthenope\" (National Coordinator: [PERSON]). The authors would like to thank [PERSON] for his scientific support.  
[PERSON] conducted the bibliographic research, performed the experiments and analyzed the data; he took part in writing the paper. [PERSON] conceived and designed the experiments and supervised data processing; he took part in writing the paper.  
The authors declare no conflict of interest.
## 5 Discussion
The skyplots indicate that both GPS and BeiDou have approximately 7 to 11 visible satellites at GPS1. GPS satellites are equally distributed over the station, but more BeiDou satellites are distributed on the southern side due to these four GEO satellites. Due to the unequal distribution of BeiDou satellites in space, its geometric dilution of precision (GDOP) will be higher than the GDOP of GPS. GDOP is a term used in satellite navigation to specify the additional multiplicative effect of navigation satellite geometry on positional measurement precision. A higher GDOP may result in a higher GOP than the other two satellites. Figure 12: The azimuth-elevation plots (skyplots) of phase residuals per satellite for station GPS1 on day 128, 8 May 2018.  
a poorer position. The visible number and the geometry of BeiDou satellites will be improved in 2019 and in the following years. It is expected that BeiDou observations will achieve similar or even better positional accuracy as GPS in China for static positioning in the near future.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 3 Result and Discussion
### Comparison Experiments
#### 3.5.2 Result Visualization  
To provide a more intuitive observation of the improved algorithm's effectiveness in fabric defect detection, this paper selects several fabric images for comparison of the detection results. Figure 8 shows the visualization results of detecting textile defects using different models under the same environment. From Figure 8a,b, it can be seen that for the detection of Pilling defects, the improved GSL-YOLOv8 algorithm achieved a detection accuracy of 0.85, which is better than the original YOLOv8n model. In the detection of small target stains, GSL-YOLOv8n exhibited the best detection accuracy. When detecting elongated defects such as Pulling Out Yarn, Float, and Skips, although GSL-YOLOv8 misclassified some textile backgrounds as stain defects, resulting in false detections, it still achieved the highest detection accuracy overall. In summary, the GSL-YOLOv8n algorithm demonstrated superior recognition accuracy in detecting textile defects of varying sizes and shapes compared to other algorithms.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 5 Experiment and Discussion  
### Experimental Dataset  
To validate the effectiveness of the proposed HybridSumm model based on the maritime judgment lexicon, we randomly selected 1800 maritime judgment documents from the corpus of 10,153 documents described in Section 3.2 as the experimental dataset. Gold-standard summaries for these documents were manually curated. The dataset was divided into a 5:1 ratio, with 1500 documents allocated to the training and validation sets using 15-fold cross-validation, and the remaining 300 documents reserved as the test set to ensure robust evaluation.  
Table 2 presents the length statistics of original maritime judgment documents and their corresponding gold-standard summaries in the experimental dataset. The data show that the original documents have an average length of 2790 characters, with 17 documents exceeding 10,000 characters. This significant length increase is primarily due to embedded raw case evidence materials and structured tabular data. After preprocessing to remove redundant attachments and tables, the effective character count of the core text was reduced to the average range, closely aligning with the length distribution of most documents.
## Materials and Methods
### Stable Isotope Analysis (SIA)  
Samples were individually stored at \(-80\)\({}^{\circ}\)C and freeze-dried for 24 h. Fish specimens were considered individually for isotopic analysis. Muscle samples were also taken from large invertebrates such as crustaceans, for which the tissue was taken from the claws, and bivalves and sea snails, whose tissue was taken from the feet [7]. When present, shells, valves and other exoskeletal parts of animals were removed under dissection microscopes in order to avoid tissue acidification before the stable isotope analysis. For small invertebrates (such as amphipods and polychaetes), the whole body was used. Samples were individually analysed. Plankton biomass was analysed as a whole due to the difficulty of obtaining sufficient biomass for isotopic analysis.  
Before the stable isotope analysis, each sample was homogenised to a fine powder using a ball mill (Mini-Mill Fritsch Pulverisette 23: Fritsch Instruments, Idar-Oberstein, Germany). When necessary, samples were pre-acidified using 1M HCl according to the drop-by-drop method [48] in order to eliminate inorganic carbon and re-dried (60 \({}^{\circ}\)C) for 72 h to remove the remaining moisture. \(\delta^{15}\)N signatures were measured in un-acidified powders to prevent acidification from interfering with the nitrogen analysis [30,49].  
Aliquots of 0.25 \(\pm\) 0.10 mg for the animals and 2.00 \(\pm\) 0.10 mg for basal resources were placed into tin capsules for C and N stable isotopic analysis (SIA). Each sample was analysed in two replicates. The analyses were carried out using a continuous flow mass spectrometer (IsoPrime100, Isoprime Ltd., Cheadle Hulme, United Kingdom) coupled with an elemental analyser (Elementar Vario Micro-Cube, Elementar Analysensysteme GmbH, Germany).  
The isotopic signatures of each sample were expressed in \(\delta\) units (\(\delta^{15}\)N; \(\delta^{13}\)C) as parts per thousand (%o) deviations from international standards (atmospheric N\({}_{2}\) for N; PD-belemite (PDB) carbonate for C), in accordance with the formula:  
\(\delta\)X (%o) = [(Rsample - Rstandard)/Rstandard] \(\times\) 10\({}^{3}\)[50], where X is \({}^{13}\)C or \({}^{15}\)N and R is the corresponding ratio of heavy to light isotope for the element (\({}^{13}\)C/\({}^{12}\)C or \({}^{15}\)N/\({}^{14}\)N). Outputs were standardised with the internal laboratory standard Caffeine IAEA-600 (C\({}_{8}\)H\({}_{10}\)N\({}_{4}\)O\({}_{2}\)). Measurement errors were found to be typically smaller than 0.05%o.
# Approximate Sensory Data Collection: A Survey
## 4 Query-Driven Approximate Data Collection Algorithms
### Coding-Based Algorithms  
The basic idea of coding-based algorithms is to use coding techniques to reduce raw sensory data while guaranteeing accuracy. Some representative works are presented as follows.  
A Bloom filter based approximate data collection protocol was studied in [51] in mobile WSNs. The nodes are partitioned into two categories, with collectors being responsible for collecting data and transmitting data to the sink, and non-collectors being responsible for sending data to reachable collectors. A collector first maps multi-dimensional values to real numbers and compresses them by the Bloom filter technique if its local buffer is full. Then, the collector transmits the stored values to the sink when the mobile sink is in its transmission range. According to the protocol, the original sensory values are hard to be recovered and the computation cost of collectors is high.  
The work in [52] proposed a sketch based approximate data collection algorithms in WSNs. The algorithm requires each sensory value to be mapped to a binary sketch in order to compress data during transmission. However, this algorithm suffers from the same problem as [51] where it is hard for the compressed data to be recovered.  
A joint coding-based data compression framework was proposed by [53] to reduce data redundancy during collection in in Wireless Multimedia Sensor Networks. The framework firstly uses an entropy-based divergence method to predict the compression efficiency of adopting the joint coding on the images collected by spatial-correlated cameras. A distributed multi-cluster coding protocol is then provided based on the prediction results. Although the framework guarantees that the overall compression rate of multimedia sensory data is maximized on the condition that the compressed data can be decoded by the sink with high precision, and the algorithm also produces redundant codings during data compression.  
The work in [54] provided a compressive data collection strategy based on a novel data structure named _compression tree_. However, the problem of finding the optimal compression tree is NP-hard; thus, the complexities of the two algorithms in [54] are high. Furthermore, the ratio bound is not acceptable in real applications.
## 3 Non-Isolated Single Tree Radio Channel Characterization
### Experimental Analysis  
In this case of non-isolated tree, measurements have been also performed in the real scenario. The transmitter has been placed at 2.5 m distance from the tree under analysis at a height of 1 m, supported by a plastic structure. The tree is 6 m height and 3.7 m of maximum width (see Figure 13). Measurements have been taken in a linear path (represented by a white dashed line in Figure 14) from the transmitter (red dot in Figure 14) to a distance of 15 m. The measured path loss values are depicted in Figure 15, where the simulation results have also been included in order to comparing them. As can be seen, in this non-isolated tree case, the simulator provides very accurate estimations. This good agreement is due to the multipath propagation, which starts gaining relevance because of the dense vegetation present in the nearer area of the tree under analysis.  
Figure 13: Detail of the Non-Isolated tree under analysis with the dense forest background.  
Figure 14: Upper view of the created scenario. The white dashed line represents the linear path where the measurements have been taken.  
Figure 15: Measured vs. Simulated Path loss for the linear path corresponding to the white dashed line of Figure 14. (**a**) at 1m height; (**b**) at 2 m height.
## 4 Case Study
### Performance of Proposed Model
#### 4.3.1 Evaluation Metrics  
The performance of the proposed model was analyzed for accuracy on ribbon width, dab diameter, dab distance, and adhesive (ribbon and dab) area by comparing the predicted value by the proposed model and the ground true. To validate the performance of the proposed model in various environments, the test was conducted with 30 images of 9 different types of adhesive shapes and ribbon thicknesses. For ground truth, there are limitations to directly measuring the irregular area and thickness of adhesive in the field. So, a photo was taken of each type from the front and annotated, and the four vertices points were extracted manually. The ground truth was obtained after performing the image transformation step (Section 3.3) and measurement step (Section 3.4) with these images and required information.  
The accuracy of width, dab diameter, and adhesive (ribbon and dab) area are calculated by Equation (12):  
\[\text{Error}=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}\frac {\left|GT_{i}-Pred_{i}\right|}{GT_{i}} \tag{12}\]  
where \(GT_{i}\) represents the ground truth of the \(i\)-th order, and \(Pred_{i}\) is the prediction of the \(i\)-th order of the sample. \(N\) is the total number of samples.  
The accuracy of the dab distance was validated by evaluating the accuracy of the dab location coordinates using RMSE, which is calculated using Equation (13). Unlike other measurements that assess a single variable at a time, dab distance requires measuring the \(x\) and \(y\) coordinates of the centroids of eight dabs in a single evaluation. Therefore, RMSE, which integrates these two variables into a single metric, was selected as the evaluation metric.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}} \Big{(}\big{(}GT_{x_{i}}-Pred_{x_{i}}\big{)}^{2}+\big{(}GT_{y_{i}}-Pred_{y_{i} }\big{)}^{2}\Big{)}} \tag{13}\]  
where \(GT_{xi}\) is the ground truth of the \(x\)-coordinate of the \(i\)-th order, \(Pred_{xi}\) is the prediction of the \(x\)-coordinate of the \(i\)-th order of the sample, \(GT_{yi}\) is the ground truth of the \(y\)-coordinate of the \(i\)-th order, and \(Pred_{yi}\) is the prediction of the \(y\)-coordinate of the \(i\)-th order of the sample. \(n\) is the total number of samples.
## 1 Introduction
### Review Methodology
#### 1.2.4 Literature Selection and Synthesis Process  
The literature selection involved initial screening based on titles and abstracts, followed by full-text review for relevant studies. Given the narrative nature of this review, the synthesis process emphasized thematic organization around key technological developments, implementation challenges, and prospects rather than quantitative meta-analysis. Particular attention was given to research addressing Chinese urban traffic characteristics and implementation contexts to better understand the applicability of various control strategies in complex urban environments with mixed traffic flows.