esa-sceva/satcom-chunk-collection huggingface dataset:
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 1 Introduction
### Literature Overview and Main Problematic
The designed circuit allowed an IWS to transmit data with 512 bits at less than a kilometer every 7 min. Note that in most IoT applications, events can occur at any time, so it is essential to provide the node with the ability to transmit data at regular time intervals. This work then dealt with the random amount of energy harvested from vibrations to supply IWSs. As mentioned above, this problem is always present in vibrational energy harvesting regardless of the electromechanical used conversion model. This work then proposes designing a predictor of the harvestable energy of vibrations to optimize the potential of the autonomous IWS. Much work has been proposed in recent years to predict environmental harvestable energy rates for powering wireless sensors [24; 25; 26]. However, these researches consider solar energy and have been facilitated by the accessibility to weather forecasts commonly provided for different regions. Note that the prediction of harvestable energy is a prerequisite for the efficient management of the harvested energy. In [26], for example, using a 7-day sun database, [PERSON] et al. have proposed a predictor of harvestable energy. Depending on the harvested power, the management module ensuring the sensor in an Energy-Neutral Operation (ENO) state has been implemented. The ENO state consists of conditioning the sensor to always lower energy expenditure than the harvested energy. Studies on predicting harvestable energy from vibrations have received little consideration, primarily due to a database lack. Secondly, numerous fluctuations appearing in some spectra of vibrations proposed in the literature have reduced researchers' interest in predicting the level of harvestable energy [12; 26]. Thus, in the design method proposed here, it will first be a question of defining a sufficient database of vibrations to establish a model making it possible to quantify the rate of harvestable power. Based on this issue, the main contributions of this paper are further described in the following subsection.
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 2 Methodology  
### DCGAN for Data Augmentation  
#### 2.1.1 GPR Image Dataset  
The measured GPR images are obtained by using a vehicle-mounted MALA GX750 model GPR instrument with a center frequency of 750 MHz, manufactured by the Swedish company GuidelineGEO, Stockholm, Sweden, acquired on a highway in China with the acquisition vehicle at a speed of 55 km/h, and are coupled with air coupling. Then, the processed images are labeled with the open-source dataset annotation tool, labelImg, to label the structural loose distresses, and a sample dataset is produced. Finally, 624 GPR images containing structural loose distresses are obtained. One image may contain multiple distresses, so a total of 670 structural loose distresses are contained in the 624 images.  
The object detection task often requires a large amount of labeled data to train the model, and only 624 images containing structural loose distresses are insufficient, so this paper uses DCGAN to expand the dataset.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 6 Conclusions  
This study designed and implemented an efficient on-chip data storage and exchange engine predicated on a superscalar pipeline for spaceborne SAR real-time processing. The storage exchange engine significantly augments data storage access bandwidth in both the range and azimuth directions while ensuring balanced data access. The utilization rate of the computing engine in both range and azimuth directions can achieve 100%. The superscalar structure's pipeline design enhances the storage and exchange efficiency of voluminous data. Experimental results reveal that the proposed data storage exchange engine, based on the superscalar pipeline, could reach 16.6 GB/s in the range direction, reflecting a 62.1% increment compared to conventional methods. The storage access bandwidth in the azimuth direction could attain 20.0 GB/s, a 95.3% increase from traditional methods. Moreover, compared to no-pipeline and scalar-pipeline designs, the storage and exchange engine yielded performance enhancements in throughput, pipeline efficiency, and speed. The feasibility of this experiment was corroborated on the \"CPU + FPGA\" heterogeneous SAR imaging processing platform integrated with this engine. The outcomes highlight that the data storage and exchange engine based on the superscalar pipeline further diminishes the imaging system's processing time, boosting processing efficiency. In the context of hardware implementation for uninhabited aerial vehicle synthetic aperture radar (UAVSAR) [46; 47] and convolutional neural networks (CNNs) [48; 49; 50; 51; 52], among other applications, the data storage and exchange method predicated on the superscalar pipeline proposed in this paper holds value. Future research will explore efficient storage and exchange methods adapted to multi-channel, multi-mode, and other real-time processing scenarios to satisfy the demands of emerging remote sensing applications.  
Conceptualization, H.L. and Y.L.; methodology, H.L., Y.L. and T.Q.; software, H.L., T.Q. and Y.X.; validation, H.L.; formal analysis, T.Q. and Y.X.; investigation, H.L., Y.L. and Y.X.; resources, T.Q. and Y.X.; writing--original draft preparation, H.L. and Y.L.; writing--review and editing, H.L., Y.L., T.Q. and Y.X.; supervision, Y.X.; project administration, Y.X. and T.Q.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
Figure 22: Schematic diagram of the performance comparison between our proposed high-efficiency data storage and exchange engine and [PERSON] et al. 2017 [33], [PERSON] et al. 2018 [42], [PERSON] et al. 2019 [34] and [PERSON] et al. 2021 [35]. **(a)** Storage access bandwidth; **(b)** Imaging processing time.  
**Funding:** This research was funded by the National Key Research and Development Program of China under Grant 2021 YFA0715204, the China ZYQN Program under Grant 2018-JCJQ-ZQ-046 and in part by the Foundation under Grant SAST2021-040.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### Sequences Comparison and Phylogenetic Analysis  
The comparison of _Bd_VgR with other insect VgRs indicated that the amino acid sequences had a similar structural feature, especially within the sequences from the same insect order (Figure 1). However, the total number of LDLRa repeats varied among different insect orders. The _Bd_VgR sequence had a Dipteran typical 13 LDLRa repeats, whereas there were 11, 12 (10 for _Bombus impatiens_), 13, 13 and 8 LDLRa repeats in Lepidoptera, Hymenoptera, Blattaria, Homoptera and Coleoptera, respectively. Besides, the O-linked sugar domains were not conserved even in the same insect order (except for Blattaria). In addition, the multiple alignments showed that _Bd_VgR shared high identity with the VgRs from Dipteran species. The identities were 41.94%, 39.37%, 81.65%, 52.71% and 61.53%, comparing to the _Bd_VgR sequence to those generated from _A. aegypti_[19], _A.gambiae_, _C. capitata_, _D. melanogaster_[21] and _M. domestica_, respectively. However, the identities were much lower when comparing _Bd_VgR with other insect VgRs. In addition, the phylogenetic tree suggested that _Bd_VgR shared a closer ancestry relationship with _Cc_VgR compared with _Md_VgR and _Dm_YPR. As expected, the VgRs from Dipteran were clustered into a separate clade from other insects (Figure 2).  
Figure 1: Diagrammatic comparison of typical domains of _B. dorsalis_ vitellogenin receptor with other insect vittogenin receptor. S, signal peptide; LBD, ligand binding domain; EGF, EGF-precursor domain; O, O-linked sugar domain; T, transmembrane domain; C, cytoplasmic domain; ID, identity; A, LDLRa; E, EGF-like repeat; Y, YWTD; _Bd_VgR, _Bactrocera dorsalis_, JX469118; _Cc_VgR, _Ceratitis capitatta_, JAC05586; _M_VgR, _Musca domestica_, XP_005190432; _Dm_VgR, _Drosophila melanogaster_, AAB60217; _da_VgR,  
_Aedes aegypti_, AAK15810; _AgVgR_, _Anopheles gambiae_, EAA06264; _AfVgR_, _Apis florea_, XP_003690500; _AmVgR_, _A. mellifera_, XP_006563364; _BiVgR_, _Bombus impatiens_, XP_003489577; _MrVgR_, _Megachile rotundata_, XP_003704660; _SiVgR_, _Solenopsis invicta_, AAP92450; _HsVgR_, _Harpegnathos saltator_, EFN84770; _Nv_VgR_, _Nasonia vitripennis_, XP_001602954; _TcVgR_, _Tribolium castaneum_, XP_968903; _NNVgR_, _Nilaparvata lugens_, ADE34166; _PhVgR_, _Pediculus humanus corporis_, EEB10383; _BgVgR_, _Blattela germanica_, CAJ 19121; _RmVgR_, _Rhyparobia maderae_, BAE93218; _PaVgR_, _Periplaneta americana_, BAC02725; _AsVgR_: _Actias selene_, AFV32171; _ApVgR_, _Antheraea pernyi_, AEJ88360; _BmVgR_, _Bombyx mori_, ADK94452; _HaVgR_, _Helicoverpa armigera_, AGF33811; _SVgR_, _Spodoptera litura_, GU983858; _DpVgR_, _Danaus plexippus_, EHJ76019.  
Figure 2: A phylogenetic tree of vitellogenin receptors. The amino acid sequences of _Bd_VgR 24 and other insect VgR sequences were selected to analyze the evolutionary relationship using the Neighbor-Joining method with a bootstrap value of 1000. The dot stands for protein sequence of VgR from _B. dorsalis_.
## 5 Sensor-Based Speech Recognition  
### Sensor-Augmented Multimodal Disambiguation System  
The intelligent disambiguation system constructed in this study adopts a modularized design to achieve high-precision Chinese homophone error correction through the multi-stage synergy of speech recognition, environment sensing, and semantic disambiguation. The workflow of the system is divided into four core stages: (1) convert the audio signal into raw text through the highly robust speech recognition module; (2) collect the acoustic environment sensor data in real time and analyze its spatial-temporal distribution characteristics; (3) perform dynamic disambiguation processing by combining the environmental parameters with the semantic context; and (4) generate a visual analysis report and error correction suggestions. Specifically, the system first utilizes the Paraformer-zh-large model for end-to-end speech recognition, and realizes streaming decoding through its unique CIF (Continuous Integrate-and-Fire) mechanism. Subsequently, the acoustic sensor collects environmental parameters such as noise, temperature, and humidity in real time, and constructs a dynamic trend map in a 60 s time window to provide environmental feature vectors for disambiguation decision-making. In the semantic processing stage, the system inputs the original text into the disambiguation model trained based on the hybrid architecture, realizes the context-aware error correction decision through the dynamic probability threshold (CONFIDENCE_THRESHOLD = 0.05), and retains the original word when the probability of the predicted word is not elevated enough to meet the threshold in order to avoid overcorrection. Finally, the system simultaneously presents audio waveforms, environmental parameter trends, and interpretable analysis of the disambiguation process through a multidimensional visualization interface.
## 3 The Proposed Method
### SNR Neural Network  
Figure 3 shows the proposed CNN structure. The inference network (Figure 3a) has five main weight layers including three convolution layers (conv) and two full connection layers (FC). In addition, it contains four batch normalization [26] layers (BN). The activation functions are all ReLU [27]. The pooling method is the maximum pooling [28]. Adam [29] is chosen as the gradient updating method. Finally, the output of the model is the standard deviation of the noise.  
Figure 3: Training and inference structures of the proposed SNR network in this paper. (**a**) The structure of the inference network; (**b**) The structure of the training network.  
Figure 2: Visualization of training structure and inference parameter integration. (**a**) Structural change; (**b**) Parameter reconstruction.  
The main training process is almost the same as the inference. The difference is in the second and third convolution process: two parallel convolutions are used instead of one single convolution (Figure 3b). The output feature maps of two parallel convolutions are performed \"plus\" an operation at the corresponding channels and pixel positions. After that, the size of the integrated feature map is the same as that of the feature map generated by one single convolution, as is the number of channels. The neural network realizes the regressively fitting task, not the classification task, thus the mean square error (MSE) is selected as the loss function. The main weight parameters of the training and inference networks are shown in Table 3.
# Sintering Behavior of a Six-Oxide Silicate Bioactive Glass for Scaffold Manufacturing
## Materials and Methods
### Thermal Analyses: DTA and HSM  
The sintering behavior of 47.5B BG was investigated by differential thermal analysis (DTA) and hot-stage microscopy (HSM) by using a DTA 404 PC instrument (Netzsch, Selb, Germany) and HSM EMI III (Hesse Instruments, Osterode am Harz, Germany), respectively; in order to simulate the same sintering treatment described for scaffold manufacturing, the thermal cycle was performed by setting a multistage program, composed as follows:  
* Step 1: from 20 to 400 \({}^{\circ}\)C, heating rate 60 \({}^{\circ}\)C/min;
* Step 2: from 400 to T\({}_{\rm s}\), heating rate 5\({}^{\circ}\)C/min;
* Step 3: 3 h-dwell, T\({}_{\rm s}\).  
For DTA measurements, 100 mg of 47.5B glass powders were introduced in Pt-Rh crucibles provided by the manufacturer and high-purity (99%) Al\({}_{2}\)O\({}_{3}\) was used in the same amount as a reference material.  
For HSM measurements, cylindrical samples of 47.5B pressed powders with a diameter of 3 mm and height of 3 mm were positioned onto a high purity alumina plate inside the furnace chamber; black and white images showing the silhouettes of the samples were acquired during the whole duration of the test and analyzed by means of a dedicated image analysis software (EMI III- Software fur das Erhitzungsmikroskop, Hesse Instruments, Osterode am Harz, Germany). Sample shrinkage \(\Delta\)H was quantified in terms of normalized height (h/h\({}_{0}\)) as a function of both time and temperature, where h\({}_{0}\) was the height of cylindrical sample at the beginning of the test (3 mm) and h was the actual height of the sample measured during the analysis.
## 5 Unicast Communication
### Key Refreshing Policy for Unicast Communication  
It is recommended that the user key ki should be renewed at set intervals, such as once every day, once per week, and so on, to ensure that the key is always in its most up-to-date state. If you decide to utilize a HASH function as the key refreshing technique, in addition to lowering the price of key distribution in the network, you can guarantee independence between the new and old keys. Before each session, the session keys ski ought to be regenerated so that the confidentiality of the information can be maintained. Because there are so many SXs, the process of refreshing the session keys in the MS and then distributing them might result in a significant increase in the cost of the network traffic. On the other hand, this problem can be resolved if they are appropriately updated in both MS and SX in accordance with the chosen method.  
If they were able to be appropriately refreshed in both MS and SX in accordance with a predetermined plan, this issue would not arise. The fact that part of the data that are utilized for key refreshing may be readily acquired by either side is the most significant aspect of the agreed-upon technique. As a result of our research, we discovered that the SX and MS were able to obtain the daily metering data of an electrical customer. This is due to the fact that metering is an essential feature of AMI systems. This has led to the creation of a unique session key refreshing approach that is based on metering data. The renewing rules for keys and variables in unicast communication are listed in Table 2.  
Figure 6: Flowchart for the unicasting communication process.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 2 Related Work
### 5G Network Environment
#### 2.2.2 5G Network Environment with ML  
This section outlines research on anomaly detection using ML in 5G mobile networks. [PERSON] et al. [21] analyzed appropriate ML models to protect 5G SDN controllers, which are targeted in DDoS attacks. The datasets used were CICDDoS201 and NSL-KDD, and the algorithms were XGBoost, RF, and logistic regression. The experimental results indicate that XGBoost is the most effective ML model.  
Based on a software-defined 5G architecture, [PERSON] et al. [22] proposed an IDS intelligence with improved software-defined technology and AI. The detection performance improved through balanced dataset training. The selected combination of ML algorithms (K-means++ and Adaboost) was also more effective than the existing solution. The datasets used were NSL-KDD and KDD Cup 1999.  
[PERSON] et al. [23] presented an ML-based DDoS attack detection model for 5G SDN networks. The dataset was used to collect specific features from DDoS attacks and normal traffic in an SDN. Four ML algorithms, i.e., SVM, NB, ANN, and KNN, were used to compare scenarios with and without feature selection. The experimental results showed that KNN with wrapper FS achieved the highest performance with an accuracy of 98.3%.  
Refs. [21; 22; 23] performed DDoS detection using ML in a 5G SDN environment. Refs. [22; 23] proposed an effective detection model using feature selection. However, this study constructed a virtual 5G core network environment, not an SDN environment. In addition, detection was performed by directly transmitting packets from the UE to the 5G core NF.
# Efficient Adsorbent-Desiccant Based on Aluminium Oxide
## 2 Methods of Obtainment of Aluminium Oxide Used in Industry
### Methods of AO Otainment
#### 2.2.1 Aluminate Technology with Alkali Treatment  
(a) Dissolution of gibbsite in alkali, accompanied by the formation of sodium aluminate:  
\[\text{Al(OH)}_{3}+\text{H}_{2}\text{O}+\text{NaOH}\rightarrow\text{NaAlO}_{2}+ \text{2H}_{2}\text{O};\]  
(b) Reprecipitation with acid:  
\[\text{NaAlO}_{2}+\text{HNO}_{3}+\text{H}_{2}\text{O}\rightarrow\text{Al(OH)}_{3 }\text{ (bayerite)}+\text{NaNO}_{3}\text{ pH}=\text{10-12};\]  
\[\text{NaAlO}_{2}+\text{HNO}_{3}\rightarrow\text{AlO(OH)}\text{ (pseudoboehmite)}+\text{NaNO}_{3}\text{ pH}<\text{9}.\]A continuous process based on the reaction of HNO3 with NaAlO2 was described by the authors of [32]. The principle is as follows: In the first reactor, HNO3 and NaAlO2 are mixed at a temperature of 30 \({}^{\circ}\)C to 75 \({}^{\circ}\)C. Then, the resulting suspension is sent to the second reactor where it is converted into pseudoboehmite. The suspension fraction is recycled in the first reactor with a ratio of 0.1 to 3 of slurry volume per volume of mixing (NaAlO2 + HNO3). Pseudoboehmite is then removed from the second reactor. After drying, it has a specific surface area in the range of 200 m\({}^{2}\)/g to 300 m\({}^{2}\)/g.  
This method is the most common for producing aluminium gel for catalysis. Precipitation is carried out from alkaline solutions (aluminates) with acids (sulfuric, nitric, hydrochloric) or acidic solutions of salts. Various structural and texture characteristics of the resulting hydroxide are determined by the pH, temperature and nature of the anion. The crystallisation rate of pseudoboehmite is determined largely by the temperature of deposition, and bayerite is primarily determined by the pH.  
To increase the dissolution velocity, gibbsite is preliminary ground to the particle size of 10 \(\upmu\)m and/or the temperature of the reacting mixture is raised.  
There are various techniques of precipitation--with a variable and constant value of pH, two-stage (cold and hot precipitation), etc. For example, cold precipitation from the sodium aluminate solution with the sulphuric acid solution is carried out at 20-25 \({}^{\circ}\)C and pH = 9.3-9.5. Hot precipitation is performed at 90-95 \({}^{\circ}\)C and pH = 9.3-9.5. Then, both modifications of aluminium oxide are mixed, and a precipitate consisting of pseudoboehmite and boehmite is obtained, which can be rinsed and filtered very well. Granules of active aluminium oxide of high mechanical strength can be obtained using this technique.  
The drawback of this method is the high cost of removing sodium due to the difficulty of rinsing the gel. The aluminium hydroxide precipitate is filtered, rinsed on a filter press and moulded into granules, which are later dried and calcined at 670-820 K to obtain \(\upeta\)- or \(\upgamma\)-Al2O3.
## 2 The Symbolic Compositional Hierarchical Model
### Model Description  
#### 2.1.1 Compositional Layers  
The SymCHM consists of an input layer \(\mathcal{L}_{0}\) and several compositional layers \(\{\mathcal{L}_{1},\ldots,\mathcal{L}_{N}\}\). Each compositional layer \(\mathcal{L}_{n}\) contains a set of parts \(\{P_{1}^{n},\ldots,P_{M_{n}}^{n}\}\), which are formed as compositions of parts from the previous layer \(\mathcal{L}_{n-1}\). The parts on the layer \(\mathcal{L}_{n-1}\) may form any number of compositions on the layer \(\mathcal{L}_{n}\), which enables their effective reuse and thus learning of compact models, as shown later in this paper. A hierarchy of parts is illustrated in Figure 1.  
The SymCHM retains part definitions of the original CHM model. The i-th composition on the layer \(\mathcal{L}_{n}\), denoted \(P_{i}^{n}\), is defined as:  
\[P_{i}^{n}=\{P_{k_{0}}^{n-1},\{P_{k_{j}}^{n-1},(\mu_{j},\sigma_{j})\}_{j=1}^{K -1}\}. \tag{1}\]  
\(P_{i}^{n}\) is a composition of \(K\) parts from the layer \(\mathcal{L}_{n-1}\), called subparts. The composition is governed by parameters \(\mu_{1,\ldots,K-1}\) and \(\sigma_{1,\ldots,K-1}\), which model relationships between the subparts. In contrast to most existing hierarchical and deep approaches, the CHM encodes compositions in a relative rather than absolute manner. This is achieved by encoding the relative distance (offset) between each subpart \(P_{k_{j}}^{n-1}\), from the first subpart \(P_{k_{0}}^{n-1}\), called the central part. The offset is encoded as a Gaussian with parameters \(\mu_{j}\) and \(\sigma_{j}\). In SymCHM, offsets are modelled in semitones in the pitch domain (a semitone is the smallest musical interval commonly used in Western tonal music), thus a composition encodes the semitone distance between patterns represented by various subparts. Currently, the standard deviation \(\sigma_{j}\) is set to a small fixed value, which does not allow for deviations from the offset encoded by \(\mu_{j}\). In future work we may relax this condition to potentially achieve similar robustness as in chromatic to morphetic pitch translation [43]. As an example, the part \(P_{2}^{3}\) in Figure 1 represents a composition of two subparts with offset 2 (\(\mu=2\)), meaning its pattern is a concatenation of two sub-patterns spaced two semitones apart. All compositions and their parameters (\(\mu,\sigma\)) are learned in an unsupervised manner as explained in Section 2.2.  
Such relative encoding of knowledge enables the model to learn position-independent concepts, which in turn enables learning of compact models from small datasets, which still generalize well [41]. This is an advantage over most neural network deep approaches, which encode concepts in an absolute manner and therefore need very large datasets to train properly.