esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 3 Preliminaries and Background
### Proofs of Ownership  
[PERSON] et al. proposed a protocol for legitimate data ownership proof. The proposed protocol is a Merkle-tree-based ownership proof protocol [17]. It allows individuals to assert ownership of data that they actually possess by presenting a portion of them to cloud storage. Proof of ownership (PoW) is a protocol used to verify whether a client has legitimate rights to access data stored in the cloud storage. When a client requests to download data, the cloud storage checks the ownership of the client. To establish the legitimacy of ownership, the client must respond appropriately to a challenge presented by the cloud storage. This response enables the cloud storage to determine whether the client has the necessary access rights to the data.  
The client uses the following algorithm to encrypt the data and generate the tag value for PoW.  
* \(\text{KeyGen}(h,m)\). Given a cryptographic hash function \(h\) and plaintext \(m\) as input, an encryption key \(K=h(m)\) is produced.
* \(\text{Encrypt}(K,m)\). Given an encryption key \(K\) and plaintext \(m\) as input, it produces encrypted data \(C\).
* \(\text{TagGen}(h,C,b)\). Given a cryptographic hash function \(h\), ciphertext \(C\), and the Merkle tree leaf size parameters \(b\) as input, it produces a Merkle tree \(MK\) and integrity verification tag \(T=MT_{b}(C)\).  
The client stores the previously generated values \(K\), \(T\), and \(MT\). It then sends \(T\) and the number of lowest-level leaf nodes to the cloud storage server. If the server finds a matching tag for \(T\), it will request ownership proof from the client. In response, the client should provide the requested node and sibling path. However, if no matching tag exists, the cloud storage will request the client to upload encrypted data.  
* \(\text{Decrypt}(K,C)\). Given an encryption key \(K\) and ciphertext \(C\) as input, it produces decrypted data \(m\).  
The client transmits \(T\) and the number of lowest-level leaf nodes to the cloud storage server. If the cloud storage server finds a matching tag, it will request ownership proof from the client. In response, the client must provide the appropriate node and sibling path as requested by the cloud storage. Upon successful ownership proof, the cloud storage will send encrypted data to the client. The client can then decrypt the data using the decryption algorithm mentioned above.
## 4 Problem Complexity
### Proof of Monotonicity  
Lemma 1.: _The objective function of cache hit rate in Subproblem 2 is monotonically increasing._  
Proof.: The definition of an increasing set function is that the value of the set function increases as you keep adding arbitrary elements to the set of independent variables. Suppose the set of cached content is \(C\), the full set containing all content is \(N\), and the set of recommended content is \(A\). Then, the hit rate is  
\[H(A)=1-\frac{\sum_{i\in N/C}(F_{i}+R_{i})}{\sum_{i\in N/\{e\}}(F_{i }+R_{i})+F_{e}}\] \[=1-\frac{\sum_{i\in N/C}F_{i}}{\sum_{i\in N/\{e\}}(F_{i}+R_{i})+F_ {e}},\]  
where \(N/C\) denotes the set of uncached content, \(N/\{e\}\) denotes the set of all content except content \(e\), and \(F_{e}\) denotes the frequency of content \(e\). Since the recommended content is obtained only from the cached content, there are \(A\subset C\) and \(A\cap(N/C)=\emptyset\), so \(R_{i}=0,\forall i\in N/C\). The above equation states that the hit rate \(H(A)\) equals 1 minus the sum of probabilities of all uncached content when the recommendation is done only on the cache's content, i.e., recommended content \(A\subset C\).  
When recommending an arbitrary content \(e\), the set of recommendations becomes \(A\cup\{e\}\), and the hit rate is  
\[H(A\cup\{e\})=1-\frac{\sum_{i\in N/C}F_{i}}{\sum_{i\in N/\{e\}}(F_{i}+R_{i})+ F_{e}+R_{e}},\]  
The hit increment is  
\[\Delta H=H(A\cup\{e\})-H(A)\] \[=\frac{R_{e}\cdot\sum_{i\in N/C}Fi}{[\sum_{i\in N/\{e\}}(F_{i}+R_ {i})+F_{e}][\sum_{i\in N/\{e\}}(F_{i}+R_{i})+F_{e}+R_{e}]}.\]  
Since both \(R_{i}\) and \(F_{i}\) are greater than or equal to 0, \(\Delta H\geq 0\), so \(H\) is monotonically increasing.
# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Wireless Spectrum Management
They look into deep multi-agent supervised online learning to enable learning in an unknown environment by predicting spectrum usage for upcoming slots at each node. They also construct a loss function that can be used to optimize predictions based on partially observable data. The five essential components of the proposed approach's architecture are the spectrum monitor, preprocessing unit, a prediction unit, probability matrix, and transmission scheduler. The first four units are on each node, and the scheduler can be centralized or decentralized. The monitoring unit can capture the energy of the surrounding spectrum. This information is forwarded to the processing unit, preparing the observation to feed the predictor unit. The latter predicts the used slots in the upcoming super-frame used by the INC. As a result, it indicates if the slot is predicted as highly used or free. The predictor unit uses a DNN in a fully connected eight-layer model to optimize the prediction. They also recommended employing a swish activation function on all layers except the last (output), which uses a SoftMax activation on the preceding dimension. The SoftMax activation function ensures that each cell in the output matrix, which reflects the prediction, has a value between 0 and 1, indicating the likelihood of the INC using the slot. The information from the predictor is used to form a probability matrix; this matrix is the basis of the operation of the scheduler to select slots expected to be free to avoid collisions. An application scenario could be where a sender node transmits a slot request to the receiver. The receiver replies with the best available slots according to the prediction. As a result, there is a channel synchronization between sender and receiver to avoid a collision. To evaluate the algorithm's performance, they used two methods: simulation and real experimentation. In the case of simulation, a multiple frequencies time division multiple access (MF-TDM) discrete event simulator was used based on the time synchronized channel hopping (6 TiSCH) simulator [31]. For real experimentation, the testbed proposed in the spectrum collaboration challenge (SC2) of the DARPA competition was used [32]. The results obtained in the simulation showed that the number of inter-networks collisions can be reduced by 30% compared to commonly used schedulers. In the case of real experimentation, the algorithm was shown to increase the overall throughput of the network in a variety of topologies and settings compared to an exponentially weighted moving average (EWMA) collision avoidance slot selection algorithm.
## 2 3D Measurement Point Cloud Acquisition and Preprocessing
### Track Slab Solid Model and Feature Establishment  
The obtained single supporting block point cloud data contains many inspection indicators, such as supporting block plane, pre-embedded casing, single rail vise spacing (small jaw distance), etc. This paper will mainly discuss supporting block plane detection method.  
The core inspection index of the supporting block plane as the track slab inspection is the key to the quality control of the track slab dimensions. Before the extraction of the supporting block plane point cloud data, the physical modeling of the track slab is carried out firstly, and the basic geometric features are established. Secondly, the point cloud data is denoised and spliced. Finally, the point cloud data is registered with the design model. The segmentation of point cloud data is the basis of point cloud computing and it plays a crucial role in subsequent plane extraction. Point cloud data segmentation methods can be broadly classified into model-driven and data-driven [23]. This paper will adopt the model-driven method, which is a top-down method. The model-driven method works as follows: The feature library is built firstly, and then the point cloud is matched with the predefined features to obtain the feature-based point cloud segmentation results. Model-based segmentation can find several rules for segmentation regions, helping data simplification, and thus speeding up data reconstruction and object recognition [24].  
The basic unit of the part model is the feature, and the establishment of features is based on the sketch. The model of the supporting block and the track bottom slab part is respectively established, and the complete track slab is further assembled to complete the construction of the track slab model.  
The parameters of the track slab model are obtained through the design drawing and point cloud reverse engineering. The design includes the parameters such as the length, width, thickness, and fastener spacing of each type of track slab. The 3D model is drawn according to the geometric relationship of the design parameters and the basic geometric features are established. The modeling process is shown in Figure 2.  
Figure 1: Overall design and operation of the detection system: (**a**) The detection system overall architecture; (**b**) The system moving direction.  
The model and geometric features are established to semantically segment the acquired original point cloud data, and the original point cloud data is divided into the supporting block, the jaw surface, and the embedded circle according to the established geometric feature information.
## 2 Principle Analysis and Modelling
### Modelling and Fabrication  
In this paper, a supercapacitor-based graphene/ion gel/graphene sandwich structure is adopted to achieve the tuning of graphene. Among these, the ion gel acts as a dielectric layer and the monolayer or bilayer graphene as electrodes, as shown in Figure 2a [37]. When voltage is applied to the edge of the electrodes, the Fermi level of the graphene changes accordingly, thereby realizing the regulation of its electrical properties.  
Ion gel is a solid mixture composed of polymers and salt electrolytes. Because of its abundant anions and cations, it has good thermal stability and excellent electrical properties, appearing gelatinous at room temperature [10; 38; 39]. With the action of an electric field, the distribution of cations and anions is uneven in  
Figure 1: The conductivity of graphene with various chemical potentials and frequency.  
Figure 2: The diagram of GIG sandwich structure and regulation principle: (**a**) the schematic of GIG sandwich structure; (**b**,**c**) the distribution of charge, (**b**) without and (**c**) with bias voltage.  
obvious electric double layer (EDL) at the interface. It is worth noting that the distance between the EDLs on the interface is much smaller than that of traditional capacitors, which will produce a larger capacitance. A unit area capacitance value in the order of uF/cm can be obtained at the thickness of the um level, so it can realize low voltage regulation of the graphene energy level [15].  
In this paper, monolayer or bilayer graphene grown on 0.1 mm thick PET film by the CVD method (as shown in Figure 3a) was used to obtain the monolayer graphene/ion gel/monolayer graphene (MIM) and bilayer graphene/ion gel/bilayer graphene (BIB) samples, respectively, and conductive silver paste was printed on the edge of the graphene, as the electrode, using electrofluid printing technology, to reduce contact resistance, whose surface was then covered with copper foil to facilitate the tuning process. The ion gel was obtained by dissolving polyvinylidene fluoride-co-hexafluoropropylene (PVDF-co-HFP) and 1-ethyl-3-methylimidazole bistrifluoromethanesulfonimide ([EMIM][TFSI]) in a mass ratio of 1:4 in an acetone solution with a mass ratio of 7, and magnetically stirring at 60 \({}^{\circ}\)C until it became transparent [37; 39]. Afterwards, the acquired ion gel was spin-coated onto the graphene film, then covered with another graphene film according to the model shown in Figure 2a to obtain the GIG structure. Finally, the sample was squeezed and coated using a rolling machine and placed in an oven at 50 \({}^{\circ}\)C for two hours. Figure 3b shows the obtained GIG sample, with the dimensions of 8 \(\times\) 8 cm and total thickness of 0.3 mm, where the ion gel layer was about 50 \(\upmu\)m thick, displaying the advantages of low profile and easy conformability. Furthermore, the sample also exhibited excellent structural stability and could be bent at a large angle.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 3 The Channel Calibration Method for Bistatic ISAR System
### Calibration Coefficient Construction  
According to Equations (5) and (9), the frequency domain expression of the accumulated signal can be written as:  
\[S_{Acc}(f)=N\cdot\sigma\cdot S_{b}(f)\cdot\exp[-j2\pi(f+f_{c})\tau_{Acc}]\cdot H _{TR}(f) \tag{11}\]  
where \(\tau_{Acc}\) is the time delay, which is equal to the time delay of the echo signal in the first period, and \(N\) is the period number of the coherent period interval (CPI). The characteristics of the transmitting and receiving channels can be expressed as:  
\[H_{TR}(f)=\frac{1}{N\sigma}\cdot\frac{S_{Acc}(f)}{S_{b}(f)}\cdot\exp[j2\pi(f+ f_{c})\tau_{Acc}] \tag{12}\]  
Figure 3: The waveform of accumulated signal at different stages in the time domain.  
where \(\exp[j2\pi(f+f_{c})\tau_{Acc}]\) reflects the peak position of the pulse compression results and the influence in each period is the same. In the process of calculating the calibration coefficient, this term can be ignored; \(N\sigma\) is considered as a constant, and also can be ignored.  
By using the reciprocal of Equation (12), the calibration coefficient of the channel can be obtained.  
\[C_{bj}=S_{b}(f)/S_{Acc}(f) \tag{13}\]  
The compensation of non-ideal channel characteristics can be achieved by multiplying the echo frequency spectrum Equation (8) with the calibration coefficient Equation (13).
## 3 Construction of the Model
### Finite Element Model Assumptions and Evaluation Guidelines  
The rock-breaking process of PDC cutters is a highly intricate, non-linear phenomenon. In this study, the focus lies on investigating the rock-breaking performance of the cutter under various schemes and parameter combinations. In order to enhance computational efficiency, it becomes necessary to exclude secondary factors and make the following assumptions [16; 23]: (1) Due to the difference in hardness between the PDC cutter and the rock material, the wear of the cutter during rock breaking is ignored and considered a rigid body. (2) The rock is removed immediately upon breaking, and repeated cutting is not considered. (3) The rotational motion of the cutter at the bottom of the well is considered a linear motion.  
The interaction between the PDC cutter and the rock in the process of rock breaking is shown in Figure 7, assuming that the lateral and axial forces on the drill bit are \(F_{x}\) and \(F_{y}\), respectively, and the PDC cutter penetrates the rock by the action of the pressure of \(F_{y}\) in the process of rock breaking and is destroyed by the rotation of \(F_{x}\). In addition, its cutting surface is subjected to rock friction \(F_{s}\), cutting reaction force \(F_{t}\), and reaction force \(F_{n}\) along the axial direction; the bottom is subjected to rock reaction force \(F_{b}\); \(\alpha\) is the angle of inclination after cutting; \(d\) is the cutting depth; \(S\) is the stroke of the PDC cutter breaking rock; and the force balance equation is:  
\[F_{x} = F_{t}+F_{n}\cos\alpha+F_{s}\sin\alpha \tag{7}\] \[F_{y} = F_{b}+F_{n}\sin\alpha+F_{s}\cos\alpha \tag{8}\]  
Figure 5: PDC cutters with different shapes.  
Figure 6: Model simplification.  
The cutting force and the mechanical specific energy of the PDC cutter in the process of rock crushing are important criteria for judging its performance [14,19]. The cutting force is the resistance of the PDC cutter in the direction of cutting the rock, and when the cutting force is lower, it means that the resistance of the cutter in the process of breaking the rock is smaller, the rock is easier to break, and the wear resistance of the cutter is stronger. The mechanical specific energy represents the energy consumed by the PDC cutter to crush the rock; the smaller the value, the higher the efficiency of the PDC cutter. Combined with Figure 7, the calculation formula for mechanical specific energy is:  
\[MSE=\frac{W}{V}=\frac{F_{t}S}{ldS}=\frac{F_{t}}{ld} \tag{9}\]  
where \(MSE\) is the mechanical specific energy (J/cm\({}^{3}\)), \(W\) is the energy consumed by breaking rock (J), \(V\) is the volume of broken rock (cm\({}^{3}\)), and \(l\) is the width of the cutter (mm).  
For the PDC bit, the ROP and the footage during rock breaking are effective criteria to measure its performance [26], and the higher ROP of the bit at a certain time means a greater feed rate and higher efficiency.
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 4 The Speak Correct Interactive Experiments
### User History Evaluation in Speak Correct  
The dataset for Speak Correct contains statements from 40 respondents. In the IT Institution, all of the trainees were indigenous Arabic women and men. Every trainee recommended that the Speak Correct interface be tested with ten illustrations for every lesson. Such instances were chosen during the individual liberty selection process to verify the incorporation of the Speak Correct data source (12,756 utterances). The dataset was divided into two sections: calibration and evaluation.  
Different particulars of the user history assessment can be demonstrated as arrangements of instances [e1, e2... en] that were registered previously during the interaction of Speak Correct. During the interaction of the Speak Correct system, in which some mistakes can be matched, the system ensures that each correction result of the phoneme sequences, pi, are different from the non-corrected phonemes. The following algorithm of the user assessment was presented in the Speak Correct evaluation algorithm (below). It is word-based correction, not corrected performance, with four different kinds of experimentation used in this proposed algorithm: \"start changing the standard of testing/change associated instance,\" \"stop/end\" assessment, and \"change the level of verification pertaining illustration\". The original decision, \"It is correct\", is used to proceed and recognize the new Speak Correct words. The second determination, \"It is not correct,\" is used to rectify the speech or phoneme pronunciation. To modify or and choose another standard or instance, use \"Modify the standard of checking associated instance.\" The final option is \"stop/end,\" that can be used to finish the Speak Correct assessment algorithm.  
Figure 4 shows the evaluation algorithm for the Speak Correct computerized interface.  
Figure 3: Relationship between recognition accuracy and the GPP values for the phonemes in the Speak Correct system.  
The empirical assessment of the Speak Correct word pronunciation assignment is discussed in the next section. Initially, authors compared the collected findings before as well as after the training modules to assess Speak Correct's effectiveness.
## 2 Specialized Metabolic Pathways for Supply of Alternative Precursors for Secondary Metabolism
### Alternative Metabolism in Actinobacteria--Ethanolamine Metabolism  
A naturally occurring common monoamine is ethanolamine (or monoethanolamine), which is a primary amine and primary alcohol. It belongs to the class of aliphatic amino alcohols. In phospholipids, ethanolamine is a nitrogenous base. It is a building block of biomembranes along with glycerol, phosphoric acid and fatty acid esters [70]. Ethanolamine is a common component of cell membranes in the form of the phosphatidylcholineethanolamine, which is the second most abundant head group for phospholipids. The incorporation of ethanolamine into cell membrane as well as its biological role have been studied in many Gram-positive bacteria such as _Mycobacterium_ spp. [71], _Corynebacterium_ spp., _Listeria_ spp., _Enterococcus_ spp. and _Clostridium_ spp. [70; 71; 72] and recently in _S. coelicolor_[39], showing its importance for cellular homeostasis.  
In Actinobacteria, the conversion of ethanolamine to glycine via a pathway with intermediates glycoaldehyde and glyoxylate was reported in _Mycobacterium_ spp. 607 [71]. Reductive amination of glyoxylate to glycine by glycine dehydrogenase has been reported in _M. tuberculosis_. However, it was proposed that, in _Mycobacterium_ spp., ethanolamine is not directly utilized as a C- or N-source as it is predominantly transformed into phos  
Figure 4: Combined scheme of polyamine biosynthetic and utilization pathways in _Streptomyces_ (modified from [43; 69]).  
phatidylethanolamine, a component of cell membranes, through a pathway that includes ethanolamine phosphate and phosphatidylserine as intermediates (the process is also described as the biosynthetic utilization of ethanolamine) [71; 72]. In _S. coelicolor_, the glutamylation of ethanolamine was confirmed recently and steps of the ethanolamine utilization pathway were postulated [39]. This pathway allows for the catabolism of ethanolamine as a C- and N-source when it is present in excess. Ethanolamine is glutamylated by GlnA4, a gamma-glutamylethanolamide synthetase. Afterwards, the pathway may require a predicted gamma-glutamylethanolamine dehydrogenase (SCO1611), a predicted gamma-glutamylaldehyde dehydrogenase (SCO1612) and a predicted gamma-glutamylglycine amidohydrolase (SCO1615). The pathway may end in the production of glycine and glutamate [39].
## 3 Proposed Approach
### Forbidden Sector Detection
In this situation, we still claim the two vectors are on the same side as this weighs towards concluding that the adversary is within the tetrahedron space (pessimistic view). The Sign function shown in Algorithm 7 returns integer -1 if the numeric argument is negative, +1 if positive, and 0 otherwise. ```
0: Tetrahedron plane vertices, opposite vertex, adversary position
1:procedureIs_On_Same_Plane_Side(\(\mathbf{p}\), \(\mathbf{q}\), \(\mathbf{r}\), \(\mathbf{v}\), \(\mathbf{a}\))
2: Compute normal to the plane spawned by \(\mathbf{q}-\mathbf{p}\) and \(\mathbf{r}-\mathbf{p}\):
3:\(\mathbf{n}\leftarrow(\mathbf{q}-\mathbf{p})\times(\mathbf{r}-\mathbf{p})\)\(\triangleright\)Cross-product
4: Compute dot products between plane normal and vectors towards \(\mathbf{v}\) and \(\mathbf{a}\):
5:\(d1\leftarrow\mathbf{n}\cdot(\mathbf{v}-\mathbf{p})\)
6:\(d2\leftarrow\mathbf{n}\cdot(\mathbf{a}-\mathbf{p})\)
7:ifSign(\(d1\)) = Sign(\(d2\)) then
8:returnTrue
9:else
10:returnFalse
11:endif
12:endprocedure
```  
**Algorithm 7** Check if opposite vertex and adversary are on the same side of the tetrahedron plane.