esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
## 4 Experimental Results
### Evaluation Metrics  
The following metrics are used to test the efficiency of the proposed model [35; 38]:  
\[\textit{accuracy}=\frac{(\mathbf{T_{N}}+\mathbf{T_{P}})}{(\mathbf{T_{P}}+ \mathbf{F_{P}}+\mathbf{T_{N}}+\mathbf{F_{N}})}*\mathbf{100} \tag{1}\]  
\[\textit{precision}=\frac{\mathbf{T_{P}}}{\mathbf{T_{P}}+\mathbf{F_{P}}}* \mathbf{100} \tag{2}\]  
\[\textit{Recall}=\frac{\mathbf{T_{P}}}{\mathbf{T_{P}}+\mathbf{F_{N}}}* \mathbf{100} \tag{3}\]  
\[\textit{F1}-\textit{score}=\frac{2*(\textit{precision}*\textit{recall})}{( \textit{precision}+\textit{Recall})} \tag{4}\]  
\[\textit{sensitivity}=\frac{\textit{number of true positives}(\mathbf{T_{P}})}{( \textit{True positives}(\mathbf{T_{P}})+\textit{False negatives}(\mathbf{F_{N}}))} \tag{5}\]  
\[\textit{specificity}=\frac{\textit{number of true negatives}(\mathbf{T_{N}})}{( \textit{True Negatives}(\mathbf{T_{N}})+\textit{False positives}(\mathbf{F_{P}}))} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Composition** & \multicolumn{2}{c}{**Size of Image**} & \multicolumn{2}{c}{**No. of Training Images**} & \multicolumn{2}{c}{**No. of Validation Images**} & \multicolumn{2}{c}{**No. of Testing Images**} & \multicolumn{2}{c}{**The Input Shape**} \\ \hline CASIA 1.0 & 913 images & 384 \(\times\) 256 pixels and & 457 images & 229 images & 227 images & 224 \(\times\) 244 & 224 \(\times\) 244 \\ \cline{2-10}
[2] & tampered 462 original & 451 & 256 \(\times\) 384 pixels & tampered 231 original & 226 & tampered 116 original & 113 & tampered 115 original & 112 & pixels \\ \hline CASIA 2.0 & 12,613 images & 900 \(\times\) 600 pixels & 6308 images & 3154 images & 3152 images & 224 \(\times\) 244 & 224 \(\times\) 244 \\ \cline{2-10}
[2] & tampered 5122 original & 7491 & 7491 & tampered 2562 original & 3746 & tampered 1281 original & 1873 & tampered 1280 original & 1872 & pixels \\ \hline CUISIDE & 363 images & 757 \(\times\) 586 to 1152 \(\times\) & 183 images & 90 images & 90 images & 224 \(\times\) 244 & 224 \(\times\) 244 & 224 \(\times\) 244 \\ \cline{2-10}
[33] & tampered 180 original & 183 & 768 pixels & tampered 90 original & 93 & tampered 45 original & 45 & tampered 45 original & 45 & pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: The details of the CASIA 1.0, CASIA 2.0, and CUISDE datasets.
###### Abstract  
There are several by-products obtained in agricultural systems that can contribute to small-scale production systems within backyards, in this sense, the production of oyster mushrooms (_Pleurotus ostreatus_) has been integrated into the rural family economy in Mexico. After harvesting the fruiting bodies, the remainder is known as sold-out substrate, this by-product has been destined for other agricultural uses such as: medium for seedlings, vermiculture or fertilizer; however, there are studies where it has been used in the diet of bovine cattle. In this study, the effects of sold-out substrate (corn stubble) of _P. ostreatus_ in the diet of broilers with different percentages of protein, on feed conversion ratio, carcass yield, and broiler meat quality were evaluated. A total of 120 broilers Ross 308 were randomly assigned in 12 pens with concrete floor and curly sharing with an area of 3 m\({}^{2}\). During the initial period (1 to 28 days), all broilers were fed commercial feed [9] mixture. On day 29, the broilers were weighed and divided into four treatment groups and fed diets with different percentages of protein until the end of the experiment. Feed conversion ratio was significantly higher in treatment control (TC) compared to the other treatments; however, the performance parameters were not affected by the diet supplemented with sold-out substrate, likewise, the color and quality of the meat were in a normal range (48 \(<\) L* \(<\) 51) and with a good amount of crude protein. Sold-out substrate of _P. ostreatus_ from corn stubble presented a high mineral content of Mg, Na, K, Fe, Cu, and Mn suitable to be considered in diets for feeding backyard broilers.  
sold-out substrate; broilers; diets; carcass weight; feed conversion ratio Article  
Pilot Study of Diet Supplemented with Sold-Out Substrate of _Pleurotus ostreatus_ in the Feeding of Backyard Broilers
# Object-Level Semantic Map Construction for Dynamic Scenes
## 4 Results
### Object 3D Tracking for Motion Detection  
We performed object 3D tracking on TUM RGB-D dataset fr3/sit-xyz and estimated the object motion detection result.fr3/sit-xyz is a low dynamic environment dataset with a frame rate of 30 frames/s, in which the object pedestrian moved slightly. If low dynamic object motion can be detected, then high dynamic object motion can also be detected under the premise of accurate object instance segmentation. Figure 6 presents the evaluation results. The static objects, such as dining tables, TVs, and potted plants, are tracked. The motion tracking results show that the angle change against the current camera pose does not exceed 0.008 rad and that the translation change does not exceed 0.01 m. For dynamic objects, such as a pedestrian walking normally, the average change in angle against the current camera pose is 0.1776 rad, with a variance of 0.0410, and the average change in translation against the current camera pose is 0.2262 m, with a variance of 0.0308. Therefore, to find actual dynamic objects as much as possible, we set the translation threshold to a low value of 0.2 m and the angle threshold to a low value of 0.1 m to judge the dynamic and static objects in the scene.  
Figure 6: _Cont._  
Figure 6: The different objects motion detection result against the current camera pose: (**a**) Static object dining table; (**b**) static object TV; (**c**) static object potted plant; (**d**) dynamic object pedestrian.
## 4 Conclusions  
A comprehensive engine test investigation using LPG-diesel blends was carried out. The approach and the tests were designed and aimed at demonstrating the feasibility and the potential of the application of a new concept using LPG-diesel blends with any injection system modifications.  
* The first part of the activity was devoted to analyzing the effects of using LPG diesel blends with a conventional common rail diesel injector. The hydraulic characterization assessed the effects of the different compressibility and viscosity of the blends, causing an increase in the injection duration, estimated to be approximately 0.1 ms for both tests carried out. On the other hand, a reduction of the hydraulic delay of 0.35-0.4 ms, and proportionally to the LPG content in the blend, was noted. Therefore, fuel-injected values for almost double the diesel amounts are observed, at the same ET, by using the LPG blend. In general, the blends do not alter the injector performance except for the different opening and closing delays, which can be solved through an appropriate calibration of the engine map.
* Concerning the engine test campaign, the results confirmed the need for optimizing the conventional injection strategy (pilot-main) to be appropriate for the DLPG application. Indeed, the lower CN of the DLPG seems critical for HC emissions and combustion efficiency. Optimization should be oriented to improve the charge reactivity during the low-temperature heat release phase and shape the premixed combustion phase for combustion noise control.
* It is worth highlighting that LPG blends are very effective in PM emission suppression that is about 90-95% at very low NOx targets. Acceptable penalties on HC and CO are detected. Based on the presented results, DLPG20 is the best fuel compromise between engine performance and emissions at a constant engine setting.
* In general, soot-less levels can be achieved without deterioration of the thermal efficiency and test-to-test repeatability (COVIMEP), thanks to the promotion of the premixing thanks to the LPG blends.  
Future activities will be addressed to develop the fuelling system, with reference to the design and realization of a proper fuel mixing system able to vary in real-time the fuel blending ratio. Then, further activities will be oriented to optimize the engine calibration setting in the whole engine operating area to evaluate the full potential of applying this new concept.  
Data curation, R.I.; investigation, R.I. and G.D.B.; methodology, M.C.; supervision, C.B.; writing--original draft, R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge [PERSON], [PERSON] and [PERSON] for their technical support.  
The authors declare no conflict of interest.
## 4 Enabling Technologies for FSO Communication
### Adaptive Transmission
#### 4.4.2 Adaptive Transmission Techniques
This approach allows for the simultaneous adjustment of both the coding rate and transmission power to minimize power consumption while adhering to practical constraints, such as the target OP, target BER, and transmit power limits imposed by eye-safety standards. In [150], an AMC scheme is introduced as an effective method for addressing severe AT. The study explores three distinct adaptive modulation scenarios: (i) channel inversion with a fixed rate, (ii) variable-rate variable-power adaptation, and (iii) truncated channel inversion with a fixed rate. Additionally, the potential enhancements from using adaptive LDPC coded modulation (CM) are examined. The findings indicate that, under strong turbulence conditions, it is possible to withstand significant fades of 30 dB and higher [150]. Additionally, the FSO channel can exhibit a significant level of reciprocity in axially aligned bidirectional systems, meaning that the fading experienced at each end can be closely correlated [230; 231]. This characteristic allows for immediate access to CSI at the Tx, eliminating the need for a separate feedback channel. Consequently, it is justifiable to assume the availability of CSI at both the Tx and Rx, which is essential for adaptive transmission [20]. Many proposed adaptive transmission methods based on CSI in FSO communication aim to enhance the reliability and throughput of these systems. However, these methods often assume that turbulence conditions remain constant. In reality, varying turbulence intensities at the same SNR can significantly impact communication quality. This suggests that while adaptive schemes may perform well under stable turbulence, their effectiveness can diminish or even degrade when faced with changing turbulence conditions. For example, an AMC scheme designed for weak turbulence may struggle to maintain the required BER in moderate or strong turbulence, leading to reduced communication quality or interruptions. Conversely, applying an AMC scheme intended for strong turbulence in milder conditions might result in a less efficient coding and modulation choice, ultimately lowering channel utilization and bit rate. To address these challenges, it is essential to develop an enhanced AMC scheme capable of adapting to FSO channels with varying turbulence intensity. Accordingly, an enhanced AMC scheme is proposed, incorporating a hybrid switching standard for UAV-to-ground FSO communication to mitigate the adverse effects of turbulence variation. This approach incorporates a channel estimator based on Gated Recurrent Units (GRU) and utilizes a hybrid switching standard linked to the frame error rate (FER) of LDPC decoding, aiming to optimize bit efficiency across different turbulence conditions.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Weighted Fusion and Saturation Improvement  
After performing brightness compensation on each block area of the original image for enhancement, slight differences in brightness may still exist between the blocks. While the overall image brightness is improved and uniform, differences may be noticeable at the junctions of each area, and the overall image can appear \"fragmented\", requiring image fusion for a seamless result. In this regard, a weighted algorithm is used to carry out weighted fusion processing on the image, and the steps are as follows:  
1. Brightness compensation processing is performed on different blocks in Section 2.4 to obtain the enhancement results of different block regions, which are defined as \(F_{1}\), \(F_{2}\), \(F_{3}\),..., \(Fin\).
2. The weighted fusion formula calculates the image result after fusion processing, which is expressed as follows: \[k_{i}=\frac{2i}{n(n+1)}\] (20) \[F(x,y)=\sum_{i=1}^{i=n}k_{i}F_{i}\] (21) where \(n\) denotes the number of image blocks, \(i\) denotes the number after block (\(i=1,2,3,\ldots,n\)), \(F_{i}\) denotes the result after brightness compensation enhancement of each different block as described in step (1), \(k_{i}\) denotes the weighting coefficient value, and \(F(x,y)\) is the result of the fusion process.  
After the image has been weighted and fused, it is transferred back to the RGB space. While the brightness of the image has been improved through this process, there is a potential loss of saturation information. To address this problem, a multi-scale detail enhancement method of Gaussian difference [23] is used to increase details, as shown in Formula (22):  
\[D^{*}=(1-w_{1}\times\text{sgn}(D_{1}))\times D_{1}+w_{2}\times D_{2}+w_{3} \times D_{3} \tag{22}\]  
The multi-scale detail enhancement method primarily involves obtaining blurred images through the application of a Gaussian kernel to the original image, extracting different levels of image details \(D_{1}\), \(D_{2}\) and \(D_{3}\), and merging the three-level images into an overall image, where \(w_{1}\), \(w_{2}\) and \(w_{3}\) take the value of 0.3, 0.3 and 0.4, respectively, to obtain the final enhanced image.
## 5 Conclusions  
In addressing the challenge of diameter measurement during the tail process of Czochralski silicon single crystal growth, this study introduced a soft measurement model, DBN-ACO-SVR, for accurately determining the diameter of the crystal. The proposed model is built based on DBN, which intricately integrates key parameters such as temperature, crystal pulling speed, crystal mass, and crystal length. A BP neural network, informed by the MIV method, is adeptly utilized for the identification of system parameters, which is pivotal in determining the latency in the impact of the temperature and pulling speed on the crystal diameter. Furthermore, to enhance the regression capabilities of the model, the ACO is employed for fine-tuning the SVR network's parameters. The efficacy of the proposed model was rigorously validated using actual data from crystal growth processes, demonstrating its effectiveness in the soft measurement of the crystal diameter during the tail process. Comparative analysis with existing methods, including standalone DBN and SVR, underscored the superiority of the proposed methodology. The results distinctly highlight the model's advanced capabilities in providing precise diameter measurements, affirming its potential as a robust tool in the field of silicon single crystal manufacturing.  
[PERSON], [PERSON] and [PERSON] designed the work, analyzed the data, and completed the simulation. [PERSON] and [PERSON] wrote the first manuscript and everyone participated in the revision. All authors contributed to the article. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Project of Shaanxi Province of China (No.2022 QCY-LL-27).  
Not applicable.  
The author will provide raw data supporting the conclusion of this paper without reservation.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Works
### Limitations and Motivations  
Unlike the aforementioned works that focused solely on either power consumption prediction or electricity theft detection, our proposed work investigated both an accurate power consumption predictor and an electricity theft detector simultaneously. Moreover, most existing electricity theft detection methods employ supervised detectors that accurately identify known attacks but struggle against zero-day attacks. Although few works have proposed anomaly-based electricity theft detectors that are more robust against zero-day attacks, they do not address the scalability and transmission efficiency challenges related to periodic data transmission. In other words, none of the existing works in the literature address how to design an anomaly-based electricity theft detector while considering the CAT approach that enables efficient data transmission. Given these limitations, this paper proposes an accurate and efficient power consumption predictor and one-class electricity theft detector that achieves the following objectives:* Efficient Data Transmission. With the continuous growth of smart grids, the periodic data transmission approach is not scalable or efficient, and it is inevitable to depend on the CAT approach for transmitting electricity consumption readings. However, it is challenging to implement accurate power consumption prediction in the light of the CAT approach because we depend on approximated readings instead of actual consumption readings. This challenge has not been addressed in the literature, and our work marks the first attempt. Similarly, it is challenging to detect electricity theft in light of the CAT approach. While this challenge has been addressed for supervised learning CAT settings, it remains unaddressed enough in unsupervised learning. Therefore, we investigated, in this study, an accurate anomaly-based detector that is trained on the CAT readings instead of the actual consumption readings.
* Accurate Power Consumption Prediction. Accurate power prediction is essential for two reasons. On one hand, it ensures a stable and reliable power grid by providing valuable insights for energy providers. This enables them to plan for sufficient generation and transmission capacity, thereby proactively mitigating the risk of power outages or disruptions. On the other hand, our electricity theft detector relies on the readings produced by the predictor. Therefore, performing an accurate prediction is critical for the electricity theft detector's performance.
* Robustness against Zero-Day Attacks. Although the capability of supervised electricity theft detectors has been proved in the literature in accurately detecting known attacks, i.e., attacks encountered in the training phase, their performance deteriorates when tested against newly encountered attacks. To address this challenge, our proposed detector is an anomaly-based detector that is trained only on benign consumption samples. Our proposed detector is capable of detecting zero-day attacks.
# Versatile High-Throughput Platform for Focused Ultrasound In Vitro Application
## 3 Results
### Ultrasound Sound Pressure Distribution and Output Homogeneity
#### 3.1.2 HIFU Platform  
The HIFU platform was characterized with respect to the emitted pressure distribution patterns, pressure amplitude, and output homogeneity across its \(8\times 4\) elements. The study first investigated the \(-6\) dB pressure field width in the longitudinal and axial dimensions of an 8-element stack and the influence of the sound field separators between adjacent elements. Figure 9a-c compare the pressure distribution fields with and without the separation elements. The \(-6\) dB width was reduced from 7.5 mm to 5.6 mm with the inclusion of the separation elements, ensuring the primary \(-6\) dB field lobe remained within one well diameter of the 96-well plate. In the longitudinal direction, the focus was narrower with a \(-6\) dB width of 1.4 mm (Figure 9c).  
For the assessment of the homogeneity of acoustic output, an xy-scan was performed with an axial dimension corresponding to that of the well floor. Based on the known positions of the elements, the maximum pressure for each well position (Figure 10a) was extracted from the data. For statistical assessment of the applicator performance, the average output and standard deviation were calculated (Figure 10b). As with the LIFU  
Figure 8: Single element output in terms of maximum pressure and I\({}_{\text{SPPA}}\) as a function of the driving voltage (mean and standard deviation values of 4 elements) in the LIFU platform. Volume in well 250 \(\mu\)L.  
Figure 9: Lateral sound field (in the direction of an 8-element stack) without (**a**) and with (**b**) the inter-element separators. (**c**) Elevational sound field in the direction perpendicular to the stack; (**d**) Comparison of lateral pressure distribution fields with and without separators.  
applicator models, yield was defined as the number of elements whose output was greater than \(-6\) dB of the average output. Accordingly, the standard deviation of the applicator output was 19%, and the yield was 94%. The lowest element output was 41% of the average.  
The maximum focal pressure when driving the applicator with 94 V\({}_{\text{peak}}\) was 1460 kPa (p\({}_{\text{max}}\)) resulting in an I\({}_{\text{SPPA}}\) of 72 W/cm\({}^{2}\) (Figure 11).
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 3 Results
### Influence of Phage Infection on Host Gene Expression  
An average of 10 million and 9.3 million high-quality reads were generated for phage-infected and uninfected bacterial cultures, respectively. Subsequently, \(\sim\)6.9 million (phage-infected cultures) and \(\sim\)9.2 million (uninfected cultures) reads, per library, were successfully mapped to the _S. aureus_ SA515 genome and \(\sim\)3.8 million reads--to the vB_SauM-515A1 bacteriophage genome. During the infection process, the proportion of reads mapping to the bacterial genome decreased from 87% at 5 min to 42% at 30 min. In turn, the proportion of reads mapping to the phage genome increased from 10% at 5 min to 56% at 30 min (Figure S1). Multidimensional scaling (MDS) of normalized RNA-seq data (three biological replicates and six different conditions) showed a clear separation of samples by condition (Control-Infected) and clustering by time post-infection (Figure S2).  
A total of 263 differentially expressed genes (DEGs) were identified (FC \(\geq\) 121; FDR \(<\) 0.001) in the phage-infected host relative to the uninfected host (Table S2). Among them, 84 and 174 genes were solely down- and upregulated, respectively, while 5 genes changed the direction of the expression pattern. Most of the genes were differentially expressed (\(n\) = 176; 93\(\uparrow\); 83\(\downarrow\)) at the early stage of infection (5 min). At the late stages of infection, the vast majority of DEGs were upregulated (at 15 min [47\(\uparrow\); 0\(\downarrow\)] and 30 min [82\(\uparrow\); 8\(\downarrow\)]). It is worth noting that DEGs identified at 5 min post-infection mostly did not overlap with DEGs at other time points, whereas at 15 and 30 min post-infection, 39 DEGs were shared (Figure S3).